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   "source": [
    "# 任务1 赛题分析(2天)\n",
    "每一步都要认真完成，附上代码，最终效果截图\n",
    "\n",
    "## 认识数据([赛题说明](https://2019ai.futurelab.tv/contest_detail/3#contest_des))\n",
    "* 了解比赛的背景\n",
    "* 分类问题还是回归问题\n",
    "* 熟悉比赛的评分函数\n",
    "  \n",
    "## 对比赛数据做EDA\n",
    "* 数据分析\n",
    "    * 缺失值分析\n",
    "    * 特征值分析\n",
    "    * 是否有单调特征列(单调的特征列很大可能是时间)\n",
    "    * 特征nunique分布\n",
    "    * 出现在测试集中的community，但是在训练集中数量较少\n",
    "    * 统计特征值出现频次大于100的特征\n",
    "    * Label分布\n",
    "    * 不同的特征值的样本的label的分布\n"
   ]
  },
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   "source": [
    "# 赛题分析\n",
    "比赛要求参赛选手根据给定的数据集，建立模型，预测房屋租金。  \n",
    "数据集中的数据类别包括租赁房源、小区、二手房、配套、新房、土地、人口、客户、真实租金等。  \n",
    "这是典型的回归预测。  \n",
    "  \n",
    "## 1. 预测指标\n",
    "#### 回归结果评价标准采用R-Square\n",
    "**R2（R-Square）的公式为**：\n",
    "残差平方和：\n",
    "$$\n",
    "SS_{res}=\\sum\\left(y_{i}-\\hat{y}_{i}\\right)^{2}\n",
    "$$\n",
    "总平均值:\n",
    "$$\n",
    "SS_{tot}=\\sum\\left(y_{i}-\\overline{y}_{i}\\right)^{2}\n",
    "$$\n",
    "\n",
    "其中$\\overline{y}$表示$y$的平均值\n",
    "得到$R^2$表达式为：\n",
    "$$\n",
    "R^{2}=1-\\frac{SS_{res}}{SS_{tot}}=1-\\frac{\\sum\\left(y_{i}-\\hat{y}_{i}\\right)^{2}}{\\sum\\left(y_{i}-\\overline{y}\\right)^{2}}\n",
    "$$\n",
    "$R^2$用于度量因变量的变异中可由自变量解释部分所占的比例，取值范围是 0~1，$R^2$越接近1,表明回归平方和占总平方和的比例越大,回归线与各观测点越接近，用x的变化来解释y值变化的部分就越多,回归的拟合程度就越好。所以$R^2$也称为拟合优度（Goodness of Fit）的统计量。\n",
    "\n",
    "$y_{i}$表示真实值，$\\hat{y}_{i}$表示预测值，$\\overline{y}_{i}$表示样本均值。得分越高拟合效果越好。\n",
    "  \n",
    "  \n",
    "## 2. 数据概况\n",
    "\n",
    "**1.租赁基本信息：**\n",
    "* ID——房屋编号　　　　　　　　　　　　　　　　　　\n",
    "* area——房屋面积 　　　\n",
    "* rentType——出租方式：整租/合租/未知　\n",
    "* houseType——房型 　　　　　　　　　\n",
    "* houseFloor——房间所在楼层：高/中/低　　　　　\n",
    "* totalFloor——房间所在的总楼层数　　　　　　　　\n",
    "* houseToward——房间朝向　　　　　　　　　　　\n",
    "* houseDecoration——房屋装修　\n",
    "\n",
    "* tradeTime——成交日期　　　　　　　　　　　　　　　\n",
    "* tradeMoney——成交租金　\n",
    "\n",
    "**2.小区信息：**\n",
    "* CommunityName——小区名称　　　　　　　　\n",
    "* city——城市　　　　　　　　　　　　　　　　　\n",
    "* region——地区　　　　　　　　　　　　　　　\n",
    "* plate——区域板块　　　　　　　　　　　　　　\n",
    "* buildYear——小区建筑年代　　　　　　　　　　\n",
    "* saleSecHouseNum——该板块当月二手房挂牌房源数　　　\n",
    "\n",
    "**3.配套设施：**\n",
    "* subwayStationNum——该板块地铁站数量　　　　　　　\n",
    "* busStationNum——该板块公交站数量　　　　　　　　　\n",
    "* interSchoolNum——该板块国际学校的数量　　　　　　\n",
    "* schoolNum——该板块公立学校的数量　　　　　　　　\n",
    "* privateSchoolNum——该板块私立学校数量　　　　　\n",
    "* hospitalNum——该板块综合医院数量　　　　　　　　　\n",
    "* DrugStoreNum——该板块药房数量　　　　　　　　\n",
    "* gymNum——该板块健身中心数量　　　　　　　　　　\n",
    "* bankNum——该板块银行数量　　　　　　　　　　　\n",
    "* shopNum——该板块商店数量　　　　　　　　　　　\n",
    "* parkNum——该板块公园数量　　　　　　　　　　　\n",
    "* mallNum——该板块购物中心数量　　　　　　　　　\n",
    "* superMarketNum——该板块超市数量　　　　　　　\n",
    "\n",
    "**4.其他信息：**\n",
    "* totalTradeMoney——该板块当月二手房成交总金额　　　\n",
    "* totalTradeArea——该板块二手房成交总面积　　　　　　\n",
    "* tradeMeanPrice——该板块二手房成交均价　　　　　　　\n",
    "* tradeSecNum——该板块当月二手房成交套数　　　　　\n",
    "\n",
    "* totalNewTradeMoney——该板块当月新房成交总金额　　　\n",
    "* totalNewTradeArea——该板块当月新房成交的总面积　　　\n",
    "* totalNewMeanPrice——该板块当月新房成交均价　　　　　\n",
    "* tradeNewNum——该板块当月新房成交套数　　　　　　　　\n",
    "* remainNewNum——该板块当月新房未成交套数　　　　　　\n",
    "* supplyNewNum——该板块当月新房供应套数　　　　　　　\n",
    "\n",
    "* supplyLandNum——该板块当月土地供应幅数　　　　　 \n",
    "* supplyLandArea——该板块当月土地供应面积　　　　　\n",
    "* tradeLandNum——该板块当月土地成交幅数　　　　　　\n",
    "* tradeLandArea——该板块当月土地成交面积　　　　　 \n",
    "* landTotalPrice——该板块当月土地成交总价　　　　　\n",
    "* landMeanPrice——该板块当月楼板价(元/m^{2})　　　\n",
    "\n",
    "* totalWorkers——当前板块现有的办公人数　　　　　　　\n",
    "* newWorkers——该板块当月流入人口数（现招聘的人员）　\n",
    "* residentPopulation——该板块常住人口　　　　　　　　\n",
    "\n",
    "* pv——该板块当月租客浏览网页次数　　　　　　　　　　　\n",
    "* uv——该板块当月租客浏览网页总人数　　　　　　　　　　\n",
    "* lookNum——线下看房次数　　　　　　　　　　　　　　　\n",
    "　　　　\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:06:14.700476Z",
     "start_time": "2019-12-24T14:06:14.489029Z"
    },
    "id": "EDF9B091264C42AF8FD7A283FCBAEFBA",
    "jupyter": {
     "outputs_hidden": false
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   "source": [
    "#coding:utf-8\n",
    "#导入warnings包，利用过滤器来实现忽略警告语句。\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# GBDT\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "# XGBoost\n",
    "import xgboost as xgb\n",
    "# LightGBM\n",
    "import lightgbm as lgb\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:04:51.300490Z",
     "start_time": "2019-12-24T14:04:50.791850Z"
    }
   },
   "outputs": [],
   "source": [
    "#载入数据\n",
    "data_train = pd.read_csv('./train_data.csv')\n",
    "data_train['Type'] = 'Train'\n",
    "data_test = pd.read_csv('./test_a.csv')\n",
    "data_test['Type'] = 'Test'\n",
    "data_all = pd.concat([data_train, data_test], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 总体情况一览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:07:26.382770Z",
     "start_time": "2019-12-24T14:07:26.116478Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41440 entries, 0 to 41439\n",
      "Data columns (total 52 columns):\n",
      "ID                    41440 non-null int64\n",
      "area                  41440 non-null float64\n",
      "rentType              41440 non-null object\n",
      "houseType             41440 non-null object\n",
      "houseFloor            41440 non-null object\n",
      "totalFloor            41440 non-null int64\n",
      "houseToward           41440 non-null object\n",
      "houseDecoration       41440 non-null object\n",
      "communityName         41440 non-null object\n",
      "city                  41440 non-null object\n",
      "region                41440 non-null object\n",
      "plate                 41440 non-null object\n",
      "buildYear             41440 non-null object\n",
      "saleSecHouseNum       41440 non-null int64\n",
      "subwayStationNum      41440 non-null int64\n",
      "busStationNum         41440 non-null int64\n",
      "interSchoolNum        41440 non-null int64\n",
      "schoolNum             41440 non-null int64\n",
      "privateSchoolNum      41440 non-null int64\n",
      "hospitalNum           41440 non-null int64\n",
      "drugStoreNum          41440 non-null int64\n",
      "gymNum                41440 non-null int64\n",
      "bankNum               41440 non-null int64\n",
      "shopNum               41440 non-null int64\n",
      "parkNum               41440 non-null int64\n",
      "mallNum               41440 non-null int64\n",
      "superMarketNum        41440 non-null int64\n",
      "totalTradeMoney       41440 non-null int64\n",
      "totalTradeArea        41440 non-null float64\n",
      "tradeMeanPrice        41440 non-null float64\n",
      "tradeSecNum           41440 non-null int64\n",
      "totalNewTradeMoney    41440 non-null int64\n",
      "totalNewTradeArea     41440 non-null int64\n",
      "tradeNewMeanPrice     41440 non-null float64\n",
      "tradeNewNum           41440 non-null int64\n",
      "remainNewNum          41440 non-null int64\n",
      "supplyNewNum          41440 non-null int64\n",
      "supplyLandNum         41440 non-null int64\n",
      "supplyLandArea        41440 non-null float64\n",
      "tradeLandNum          41440 non-null int64\n",
      "tradeLandArea         41440 non-null float64\n",
      "landTotalPrice        41440 non-null int64\n",
      "landMeanPrice         41440 non-null float64\n",
      "totalWorkers          41440 non-null int64\n",
      "newWorkers            41440 non-null int64\n",
      "residentPopulation    41440 non-null int64\n",
      "pv                    41422 non-null float64\n",
      "uv                    41422 non-null float64\n",
      "lookNum               41440 non-null int64\n",
      "tradeTime             41440 non-null object\n",
      "tradeMoney            41440 non-null float64\n",
      "Type                  41440 non-null object\n",
      "dtypes: float64(10), int64(30), object(12)\n",
      "memory usage: 16.4+ MB\n",
      "None\n",
      "                 ID          area    totalFloor  saleSecHouseNum  \\\n",
      "count  4.144000e+04  41440.000000  41440.000000     41440.000000   \n",
      "mean   1.001221e+08     70.959409     11.413152         1.338538   \n",
      "std    9.376566e+04     88.119569      7.375203         3.180349   \n",
      "min    1.000000e+08      1.000000      0.000000         0.000000   \n",
      "25%    1.000470e+08     42.607500      6.000000         0.000000   \n",
      "50%    1.000960e+08     65.000000      7.000000         0.000000   \n",
      "75%    1.001902e+08     90.000000     16.000000         1.000000   \n",
      "max    1.003218e+08  15055.000000     88.000000        52.000000   \n",
      "\n",
      "       subwayStationNum  busStationNum  interSchoolNum     schoolNum  \\\n",
      "count      41440.000000   41440.000000    41440.000000  41440.000000   \n",
      "mean           5.741192     187.197153        1.506395     48.228813   \n",
      "std            4.604929     179.674625        1.687631     29.568448   \n",
      "min            0.000000      24.000000        0.000000      9.000000   \n",
      "25%            2.000000      74.000000        0.000000     24.000000   \n",
      "50%            5.000000     128.000000        1.000000     47.000000   \n",
      "75%            7.000000     258.000000        3.000000     61.000000   \n",
      "max           22.000000     824.000000        8.000000    142.000000   \n",
      "\n",
      "       privateSchoolNum   hospitalNum  ...  tradeLandArea  landTotalPrice  \\\n",
      "count      41440.000000  41440.000000  ...   41440.000000    4.144000e+04   \n",
      "mean           6.271911      4.308736  ...   12621.406425    1.045363e+08   \n",
      "std            4.946457      3.359714  ...   49853.120341    5.215216e+08   \n",
      "min            0.000000      0.000000  ...       0.000000    0.000000e+00   \n",
      "25%            2.000000      1.000000  ...       0.000000    0.000000e+00   \n",
      "50%            5.000000      4.000000  ...       0.000000    0.000000e+00   \n",
      "75%            9.000000      6.000000  ...       0.000000    0.000000e+00   \n",
      "max           24.000000     14.000000  ...  555508.010000    6.197570e+09   \n",
      "\n",
      "       landMeanPrice   totalWorkers     newWorkers  residentPopulation  \\\n",
      "count   41440.000000   41440.000000   41440.000000        41440.000000   \n",
      "mean      724.763918   77250.235497    1137.132095       294514.059459   \n",
      "std      3224.303831  132052.508523    7667.381627       196745.147181   \n",
      "min         0.000000     600.000000       0.000000        49330.000000   \n",
      "25%         0.000000   13983.000000       0.000000       165293.000000   \n",
      "50%         0.000000   38947.000000       0.000000       245872.000000   \n",
      "75%         0.000000   76668.000000       0.000000       330610.000000   \n",
      "max     37513.062490  855400.000000  143700.000000       928198.000000   \n",
      "\n",
      "                  pv            uv       lookNum    tradeMoney  \n",
      "count   41422.000000  41422.000000  41440.000000  4.144000e+04  \n",
      "mean    26945.663512   3089.077085      0.396260  8.837074e+03  \n",
      "std     32174.637924   2954.706517      1.653932  5.514287e+05  \n",
      "min        17.000000      6.000000      0.000000  0.000000e+00  \n",
      "25%      7928.000000   1053.000000      0.000000  2.800000e+03  \n",
      "50%     20196.000000   2375.000000      0.000000  4.000000e+03  \n",
      "75%     34485.000000   4233.000000      0.000000  5.500000e+03  \n",
      "max    621864.000000  39876.000000     37.000000  1.000000e+08  \n",
      "\n",
      "[8 rows x 40 columns]\n"
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       "      <td>1665</td>\n",
       "      <td>253337</td>\n",
       "      <td>888.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>9</td>\n",
       "      <td>2018/12/21</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>100305262</td>\n",
       "      <td>129.00</td>\n",
       "      <td>未知方式</td>\n",
       "      <td>3室2厅3卫</td>\n",
       "      <td>低</td>\n",
       "      <td>2</td>\n",
       "      <td>暂无数据</td>\n",
       "      <td>毛坯</td>\n",
       "      <td>XQ01257</td>\n",
       "      <td>SH</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>800</td>\n",
       "      <td>117</td>\n",
       "      <td>125309</td>\n",
       "      <td>2038.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018/11/18</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>Train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID    area rentType houseType houseFloor  totalFloor houseToward  \\\n",
       "0  100309852   68.06     未知方式    2室1厅1卫          低          16        暂无数据   \n",
       "1  100307942  125.55     未知方式    3室2厅2卫          中          14        暂无数据   \n",
       "2  100307764  132.00     未知方式    3室2厅2卫          低          32        暂无数据   \n",
       "3  100306518   57.00     未知方式    1室1厅1卫          中          17        暂无数据   \n",
       "4  100305262  129.00     未知方式    3室2厅3卫          低           2        暂无数据   \n",
       "\n",
       "  houseDecoration communityName city  ... landMeanPrice totalWorkers  \\\n",
       "0              其他       XQ00051   SH  ...        0.0000        28248   \n",
       "1              简装       XQ00130   SH  ...        0.0000        14823   \n",
       "2              其他       XQ00179   SH  ...        0.0000        77645   \n",
       "3              精装       XQ00313   SH  ...     3080.0331         8750   \n",
       "4              毛坯       XQ01257   SH  ...        0.0000          800   \n",
       "\n",
       "  newWorkers  residentPopulation      pv     uv  lookNum   tradeTime  \\\n",
       "0        614              111546  1124.0  284.0        0  2018/11/28   \n",
       "1        148              157552   701.0   22.0        1  2018/12/16   \n",
       "2        520              131744    57.0   20.0        1  2018/12/22   \n",
       "3       1665              253337   888.0  279.0        9  2018/12/21   \n",
       "4        117              125309  2038.0  480.0        0  2018/11/18   \n",
       "\n",
       "   tradeMoney   Type  \n",
       "0      2000.0  Train  \n",
       "1      2000.0  Train  \n",
       "2     16000.0  Train  \n",
       "3      1600.0  Train  \n",
       "4      2900.0  Train  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总体情况\n",
    "print(data_train.info())\n",
    "print(data_train.describe())\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 简要分析\n",
    "该份训练集包含 41440行×52列数据  \n",
    "目标变量是 真实房租价格- **tradeMoney** \n",
    "  \n",
    "大多数数据都是int或float型；有部分字段是object型，即文本型中文或英文的，如rentType字段，这些字段在之后需要做处理  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 分类特征和连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:05:08.172371Z",
     "start_time": "2019-12-24T14:05:08.162401Z"
    }
   },
   "outputs": [],
   "source": [
    "# 根据特征含义和特征一览，大致可以判断出数值型和类别型特征如下\n",
    "categorical_feas = ['rentType', 'houseType', 'houseFloor', 'region', 'plate', 'houseToward', 'houseDecoration',\n",
    "    'communityName','city','region','plate','buildYear']\n",
    "\n",
    "numerical_feas=['ID','area','totalFloor','saleSecHouseNum','subwayStationNum',\n",
    "    'busStationNum','interSchoolNum','schoolNum','privateSchoolNum','hospitalNum',\n",
    "    'drugStoreNum','gymNum','bankNum','shopNum','parkNum','mallNum','superMarketNum',\n",
    "    'totalTradeMoney','totalTradeArea','tradeMeanPrice','tradeSecNum','totalNewTradeMoney',\n",
    "    'totalNewTradeArea','tradeNewMeanPrice','tradeNewNum','remainNewNum','supplyNewNum',\n",
    "    'supplyLandNum','supplyLandArea','tradeLandNum','tradeLandArea','landTotalPrice',\n",
    "    'landMeanPrice','totalWorkers','newWorkers','residentPopulation','pv','uv','lookNum']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 缺失值分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:05:09.367176Z",
     "start_time": "2019-12-24T14:05:09.263457Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missingNum</th>\n",
       "      <th>existNum</th>\n",
       "      <th>sum</th>\n",
       "      <th>missingRatio</th>\n",
       "      <th>dtype</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>pv</td>\n",
       "      <td>18</td>\n",
       "      <td>41422</td>\n",
       "      <td>41440</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>uv</td>\n",
       "      <td>18</td>\n",
       "      <td>41422</td>\n",
       "      <td>41440</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       missingNum  existNum    sum  missingRatio    dtype\n",
       "index                                                    \n",
       "pv             18     41422  41440      0.043436  float64\n",
       "uv             18     41422  41440      0.043436  float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值分析\n",
    "def missing_values(df):\n",
    "    alldata_na = pd.DataFrame(df.isnull().sum(), columns={'missingNum'})\n",
    "    alldata_na['existNum'] = len(df) - alldata_na['missingNum']\n",
    "    alldata_na['sum'] = len(df)\n",
    "    alldata_na['missingRatio'] = alldata_na['missingNum']/len(df)*100\n",
    "    alldata_na['dtype'] = df.dtypes\n",
    "    #ascending：默认True升序排列；False降序排列\n",
    "    alldata_na = alldata_na[alldata_na['missingNum']>0].reset_index().sort_values(by=['missingNum','index'],ascending=[False,True])\n",
    "    alldata_na.set_index('index',inplace=True)\n",
    "    return alldata_na\n",
    "\n",
    "missing_values(data_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 简要分析\n",
    "这里采用编写函数的方式来直接获取结果（这种方式会在之后反复用到，建议大家尽早养成函数式编写的习惯）；  \n",
    "其实在总体情况一览中，info()函数也能看出来。  \n",
    "  \n",
    "结果是，仅有pv、uv存在缺失值，后面再探究会发现缺失的都是属于同一个plate，可能是官方直接删除了该plate的pv、uv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 单调特征列分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:05:35.623999Z",
     "start_time": "2019-12-24T14:05:34.738329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "单调特征： tradeTime\n",
      "单调特征值个数： 24085\n",
      "单调特征值比例： 0.5812017374517374\n"
     ]
    }
   ],
   "source": [
    "#是否有单调特征列(单调的特征列很大可能是时间)\n",
    "def incresing(vals):\n",
    "    cnt = 0\n",
    "    len_ = len(vals)\n",
    "    for i in range(len_-1):\n",
    "        if vals[i+1] > vals[i]:\n",
    "            cnt += 1\n",
    "    return cnt\n",
    "\n",
    "fea_cols = [col for col in data_train.columns]\n",
    "for col in fea_cols:\n",
    "    cnt = incresing(data_train[col].values)\n",
    "    if cnt / data_train.shape[0] >= 0.55:\n",
    "        print('单调特征：',col)\n",
    "        print('单调特征值个数：', cnt)\n",
    "        print('单调特征值比例：', cnt / data_train.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 简要分析\n",
    "先编写判断单调的函数 *incresing*， 然后再应用到每列上；  \n",
    "单调特征是 tradeTime，为时间列。  \n",
    "  \n",
    "多说句额外的，时间列在特征工程的时候，不同的情况下能有很多的变种形式，比如按年月日分箱，或者按不同的维度在时间上聚合分组，等等  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 特征nunique分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:05:49.677415Z",
     "start_time": "2019-12-24T14:05:44.166121Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rentType的特征分布如下：\n",
      "未知方式    30759\n",
      "整租       5472\n",
      "合租       5204\n",
      "--          5\n",
      "Name: rentType, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "houseType的特征分布如下：\n",
      "1室1厅1卫    9805\n",
      "2室1厅1卫    8512\n",
      "2室2厅1卫    6783\n",
      "3室1厅1卫    3992\n",
      "3室2厅2卫    2737\n",
      "          ... \n",
      "7室4厅3卫       1\n",
      "6室1厅4卫       1\n",
      "5室5厅4卫       1\n",
      "2室1厅3卫       1\n",
      "8室3厅4卫       1\n",
      "Name: houseType, Length: 104, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "houseFloor的特征分布如下：\n",
      "中    15458\n",
      "高    14066\n",
      "低    11916\n",
      "Name: houseFloor, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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b77wkSbM3yJHBVN4P7EjyEeAB4IZWvwH4VJIxOkcE6wGqam+SncBDwFFgc1U9MwfzkiRN4YSEQVV9CfhSW95Pj7uBquoHwGVTjL8auPpEzEWSNHt+A1mSZBhIkgwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkhggDJKsSHJHkoeT7E3ynlY/I8meJPva69JWT5LrkowleTDJuV3b2tD670uyYar3lCTNjUGODI4Cf1xVvwSsATYnWQ1cCdxeVcPA7W0d4GI6zzceBjYB10MnPIAtwPl0npC25ViASJLmR99hUFWPV9WX2/L3gIeBZcA6YHvrth24pC2vA26qjruBJUnOBi4C9lTV4ao6AuwB1vY7L0nS7J2QawZJVgKvA+4BXlZVj0MnMICXtm7LgANdw8Zbbaq6JGmeDBwGSX4a+AfgvVX1X8fr2qNWx6n3eq9NSUaTjE5MTMx+spKkngYKgyQ/RScIPl1Vn23l77TTP7TXQ60+DqzoGr4cOHic+rNU1daqGqmqkaGhoUGmLknqMsjdRAFuAB6uqr/satoFHLsjaANwa1f9inZX0RrgyXYaaTdwYZKl7cLxha0mSZoniwcY+0bgd4GvJflKq/0p8FFgZ5KNwGPAZa3tNuAtwBjwfeAdAFV1OMmHgftavw9V1eEB5iVJmqW+w6Cq/o3e5/sBLujRv4DNU2xrG7Ct37lIkgbjN5AlSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJLESRQGSdYmeSTJWJIrF3o+knQqOSnCIMki4OPAxcBq4PIkqxd2VpJ06jgpwgA4Dxirqv1V9TSwA1i3wHOSpFPG4oWeQLMMONC1Pg6cP7lTkk3Aprb630ke6fP9zgK+2+dYaTp+vjRncs3An6+f71U8WcIgPWr1rELVVmDrwG+WjFbVyKDbkXrx86W5NFefr5PlNNE4sKJrfTlwcIHmIkmnnJMlDO4DhpOsSnIasB7YtcBzkqRTxklxmqiqjiZ5F7AbWARsq6q9c/iWA59qko7Dz5fm0px8vlL1rFPzkqRTzMlymkiStIAMA0mSYSBJOkkuIM+HJB8E1gBHW2kxcHdVfXCh5qTnFj9jmktz/fk6ZcKgWV9VTwAkWQK8d4Hno+ceP2OaS3P2+fI0kSTJMJAkGQaSJAwDSRKGgSQJw0CSxKl1a+kh4KYkP2zrzwO+sIDz0XOPnzHNpTn9fPmP6iRJniaSJBkGkiQMA0kShoEkCcNAkgT8H5mwWJxwQ/TgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region的特征分布如下：\n",
      "RG00002    11437\n",
      "RG00005     5739\n",
      "RG00003     4186\n",
      "RG00010     3640\n",
      "RG00012     3368\n",
      "RG00004     3333\n",
      "RG00006     1961\n",
      "RG00007     1610\n",
      "RG00008     1250\n",
      "RG00013     1215\n",
      "RG00001     1157\n",
      "RG00014     1069\n",
      "RG00011      793\n",
      "RG00009      681\n",
      "RG00015        1\n",
      "Name: region, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plate的特征分布如下：\n",
      "BK00031    1958\n",
      "BK00033    1837\n",
      "BK00045    1816\n",
      "BK00055    1566\n",
      "BK00056    1516\n",
      "           ... \n",
      "BK00016      40\n",
      "BK00036      33\n",
      "BK00058      15\n",
      "BK00032       3\n",
      "BK00001       1\n",
      "Name: plate, Length: 66, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "houseToward的特征分布如下：\n",
      "南       34377\n",
      "南北       2254\n",
      "北        2043\n",
      "暂无数据      963\n",
      "东南        655\n",
      "东         552\n",
      "西         264\n",
      "西南        250\n",
      "西北         58\n",
      "东西         24\n",
      "Name: houseToward, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "houseDecoration的特征分布如下：\n",
      "其他    29040\n",
      "精装    10918\n",
      "简装     1171\n",
      "毛坯      311\n",
      "Name: houseDecoration, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "communityName的特征分布如下：\n",
      "XQ01834    358\n",
      "XQ01274    192\n",
      "XQ02273    188\n",
      "XQ03110    185\n",
      "XQ02337    173\n",
      "          ... \n",
      "XQ01302      1\n",
      "XQ02870      1\n",
      "XQ01972      1\n",
      "XQ02671      1\n",
      "XQ01787      1\n",
      "Name: communityName, Length: 4236, dtype: int64\n",
      "city的特征分布如下：\n",
      "SH    41440\n",
      "Name: city, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region的特征分布如下：\n",
      "RG00002    11437\n",
      "RG00005     5739\n",
      "RG00003     4186\n",
      "RG00010     3640\n",
      "RG00012     3368\n",
      "RG00004     3333\n",
      "RG00006     1961\n",
      "RG00007     1610\n",
      "RG00008     1250\n",
      "RG00013     1215\n",
      "RG00001     1157\n",
      "RG00014     1069\n",
      "RG00011      793\n",
      "RG00009      681\n",
      "RG00015        1\n",
      "Name: region, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plate的特征分布如下：\n",
      "BK00031    1958\n",
      "BK00033    1837\n",
      "BK00045    1816\n",
      "BK00055    1566\n",
      "BK00056    1516\n",
      "           ... \n",
      "BK00016      40\n",
      "BK00036      33\n",
      "BK00058      15\n",
      "BK00032       3\n",
      "BK00001       1\n",
      "Name: plate, Length: 66, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buildYear的特征分布如下：\n",
      "1994    2851\n",
      "暂无信息    2808\n",
      "2006    2007\n",
      "2007    1851\n",
      "2008    1849\n",
      "        ... \n",
      "1972       2\n",
      "1961       2\n",
      "1962       1\n",
      "1950       1\n",
      "1951       1\n",
      "Name: buildYear, Length: 80, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征nunique分布\n",
    "for feature in categorical_feas:\n",
    "    print(feature + \"的特征分布如下：\")\n",
    "    print(data_train[feature].value_counts())\n",
    "    if feature != 'communityName': # communityName值太多，暂且不看图表\n",
    "        plt.hist(data_all[feature], bins=3)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:05:49.713284Z",
     "start_time": "2019-12-24T14:05:49.681367Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XQ01834    358\n",
      "XQ01274    192\n",
      "XQ02273    188\n",
      "XQ03110    185\n",
      "XQ02337    173\n",
      "          ... \n",
      "XQ01302      1\n",
      "XQ02870      1\n",
      "XQ01972      1\n",
      "XQ02671      1\n",
      "XQ01787      1\n",
      "Name: communityName, Length: 4236, dtype: int64\n",
      "XQ03306    1\n",
      "XQ00629    1\n",
      "XQ01830    1\n",
      "XQ00534    1\n",
      "XQ02021    1\n",
      "          ..\n",
      "XQ03596    1\n",
      "XQ03784    1\n",
      "XQ00332    1\n",
      "XQ01140    1\n",
      "XQ00239    1\n",
      "Name: communityName, Length: 2469, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data_train['communityName'].value_counts())\n",
    "print(data_test['communityName'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 简要分析\n",
    "用自带函数value_counts() 来得到每个分类变量的 种类 分布；  \n",
    "并且简单画出柱状图。  \n",
    "  \n",
    "rentType：4种，且绝大多数是无用的未知方式；  \n",
    "houseType：104种，绝大多数在3室及以下；  \n",
    "houseFloor：3种，分布较为均匀；  \n",
    "region：       15种；  \n",
    "plate：        66种；  \n",
    "houseToward：    10种；  \n",
    "houseDecoration： 4种，一大半是其他；  \n",
    "buildYear：     80种；  \n",
    "communityName：   4236种，且分布较为稀疏；  \n",
    "  \n",
    "此步骤是为之后数据处理和特征工程做准备，先理解每个字段的含义以及分布，之后需要根据实际含义对分类变量做不同的处理。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 统计特征值频次大于100的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:05:51.911403Z",
     "start_time": "2019-12-24T14:05:51.761806Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  rentType  counts\n",
      "0     未知方式   30759\n",
      "1       整租    5472\n",
      "2       合租    5204\n",
      "   houseType  counts\n",
      "0     1室1厅1卫    9805\n",
      "1     2室1厅1卫    8512\n",
      "2     2室2厅1卫    6783\n",
      "3     3室1厅1卫    3992\n",
      "4     3室2厅2卫    2737\n",
      "5     4室1厅1卫    1957\n",
      "6     3室2厅1卫    1920\n",
      "7     1室0厅1卫    1286\n",
      "8     1室2厅1卫     933\n",
      "9     2室2厅2卫     881\n",
      "10    4室2厅2卫     435\n",
      "11    2室0厅1卫     419\n",
      "12    4室2厅3卫     273\n",
      "13    5室1厅1卫     197\n",
      "14    2室1厅2卫     155\n",
      "15    3室2厅3卫     149\n",
      "16    3室1厅2卫     135\n",
      "  houseFloor  counts\n",
      "0          中   15458\n",
      "1          高   14066\n",
      "2          低   11916\n",
      "     region  counts\n",
      "0   RG00002   11437\n",
      "1   RG00005    5739\n",
      "2   RG00003    4186\n",
      "3   RG00010    3640\n",
      "4   RG00012    3368\n",
      "5   RG00004    3333\n",
      "6   RG00006    1961\n",
      "7   RG00007    1610\n",
      "8   RG00008    1250\n",
      "9   RG00013    1215\n",
      "10  RG00001    1157\n",
      "11  RG00014    1069\n",
      "12  RG00011     793\n",
      "13  RG00009     681\n",
      "      plate  counts\n",
      "0   BK00031    1958\n",
      "1   BK00033    1837\n",
      "2   BK00045    1816\n",
      "3   BK00055    1566\n",
      "4   BK00056    1516\n",
      "5   BK00052    1375\n",
      "6   BK00017    1305\n",
      "7   BK00041    1266\n",
      "8   BK00054    1256\n",
      "9   BK00051    1253\n",
      "10  BK00046    1227\n",
      "11  BK00035    1156\n",
      "12  BK00042    1137\n",
      "13  BK00009    1016\n",
      "14  BK00050     979\n",
      "15  BK00043     930\n",
      "16  BK00026     906\n",
      "17  BK00047     880\n",
      "18  BK00034     849\n",
      "19  BK00013     834\n",
      "20  BK00053     819\n",
      "21  BK00028     745\n",
      "22  BK00040     679\n",
      "23  BK00060     671\n",
      "24  BK00010     651\n",
      "25  BK00029     646\n",
      "26  BK00062     618\n",
      "27  BK00022     614\n",
      "28  BK00018     613\n",
      "29  BK00064     590\n",
      "30  BK00005     549\n",
      "31  BK00003     523\n",
      "32  BK00014     500\n",
      "33  BK00019     498\n",
      "34  BK00061     477\n",
      "35  BK00011     455\n",
      "36  BK00037     444\n",
      "37  BK00012     412\n",
      "38  BK00038     398\n",
      "39  BK00024     397\n",
      "40  BK00020     384\n",
      "41  BK00002     357\n",
      "42  BK00065     348\n",
      "43  BK00027     344\n",
      "44  BK00039     343\n",
      "45  BK00063     281\n",
      "46  BK00057     278\n",
      "47  BK00015     253\n",
      "48  BK00006     231\n",
      "49  BK00021     226\n",
      "50  BK00007     225\n",
      "51  BK00066     219\n",
      "52  BK00030     219\n",
      "53  BK00049     211\n",
      "54  BK00008     210\n",
      "55  BK00004     189\n",
      "56  BK00048     165\n",
      "57  BK00025     157\n",
      "58  BK00023     127\n",
      "59  BK00059     122\n",
      "  houseToward  counts\n",
      "0           南   34377\n",
      "1          南北    2254\n",
      "2           北    2043\n",
      "3        暂无数据     963\n",
      "4          东南     655\n",
      "5           东     552\n",
      "6           西     264\n",
      "7          西南     250\n",
      "  houseDecoration  counts\n",
      "0              其他   29040\n",
      "1              精装   10918\n",
      "2              简装    1171\n",
      "3              毛坯     311\n",
      "   communityName  counts\n",
      "0        XQ01834     358\n",
      "1        XQ01274     192\n",
      "2        XQ02273     188\n",
      "3        XQ03110     185\n",
      "4        XQ02337     173\n",
      "5        XQ01389     166\n",
      "6        XQ01658     163\n",
      "7        XQ02789     152\n",
      "8        XQ00530     151\n",
      "9        XQ01561     151\n",
      "10       XQ01339     132\n",
      "11       XQ01873     122\n",
      "12       XQ00826     122\n",
      "13       XQ02296     121\n",
      "14       XQ01232     119\n",
      "15       XQ01401     118\n",
      "16       XQ02441     117\n",
      "17       XQ00196     115\n",
      "18       XQ01207     109\n",
      "19       XQ02365     109\n",
      "20       XQ01410     108\n",
      "21       XQ00852     105\n",
      "22       XQ02072     103\n",
      "23       XQ01672     103\n",
      "  city  counts\n",
      "0   SH   41440\n",
      "     region  counts\n",
      "0   RG00002   11437\n",
      "1   RG00005    5739\n",
      "2   RG00003    4186\n",
      "3   RG00010    3640\n",
      "4   RG00012    3368\n",
      "5   RG00004    3333\n",
      "6   RG00006    1961\n",
      "7   RG00007    1610\n",
      "8   RG00008    1250\n",
      "9   RG00013    1215\n",
      "10  RG00001    1157\n",
      "11  RG00014    1069\n",
      "12  RG00011     793\n",
      "13  RG00009     681\n",
      "      plate  counts\n",
      "0   BK00031    1958\n",
      "1   BK00033    1837\n",
      "2   BK00045    1816\n",
      "3   BK00055    1566\n",
      "4   BK00056    1516\n",
      "5   BK00052    1375\n",
      "6   BK00017    1305\n",
      "7   BK00041    1266\n",
      "8   BK00054    1256\n",
      "9   BK00051    1253\n",
      "10  BK00046    1227\n",
      "11  BK00035    1156\n",
      "12  BK00042    1137\n",
      "13  BK00009    1016\n",
      "14  BK00050     979\n",
      "15  BK00043     930\n",
      "16  BK00026     906\n",
      "17  BK00047     880\n",
      "18  BK00034     849\n",
      "19  BK00013     834\n",
      "20  BK00053     819\n",
      "21  BK00028     745\n",
      "22  BK00040     679\n",
      "23  BK00060     671\n",
      "24  BK00010     651\n",
      "25  BK00029     646\n",
      "26  BK00062     618\n",
      "27  BK00022     614\n",
      "28  BK00018     613\n",
      "29  BK00064     590\n",
      "30  BK00005     549\n",
      "31  BK00003     523\n",
      "32  BK00014     500\n",
      "33  BK00019     498\n",
      "34  BK00061     477\n",
      "35  BK00011     455\n",
      "36  BK00037     444\n",
      "37  BK00012     412\n",
      "38  BK00038     398\n",
      "39  BK00024     397\n",
      "40  BK00020     384\n",
      "41  BK00002     357\n",
      "42  BK00065     348\n",
      "43  BK00027     344\n",
      "44  BK00039     343\n",
      "45  BK00063     281\n",
      "46  BK00057     278\n",
      "47  BK00015     253\n",
      "48  BK00006     231\n",
      "49  BK00021     226\n",
      "50  BK00007     225\n",
      "51  BK00066     219\n",
      "52  BK00030     219\n",
      "53  BK00049     211\n",
      "54  BK00008     210\n",
      "55  BK00004     189\n",
      "56  BK00048     165\n",
      "57  BK00025     157\n",
      "58  BK00023     127\n",
      "59  BK00059     122\n",
      "   buildYear  counts\n",
      "0       1994    2851\n",
      "1       暂无信息    2808\n",
      "2       2006    2007\n",
      "3       2007    1851\n",
      "4       2008    1849\n",
      "5       2005    1814\n",
      "6       2010    1774\n",
      "7       1995    1685\n",
      "8       1993    1543\n",
      "9       2011    1498\n",
      "10      2004    1431\n",
      "11      2009    1271\n",
      "12      2014    1238\n",
      "13      2003    1156\n",
      "14      1997    1125\n",
      "15      2002    1120\n",
      "16      2012    1049\n",
      "17      1996     991\n",
      "18      2000     925\n",
      "19      2001     898\n",
      "20      2015     840\n",
      "21      1999     822\n",
      "22      1998     733\n",
      "23      2013     714\n",
      "24      1987     632\n",
      "25      1983     612\n",
      "26      1991     545\n",
      "27      1984     493\n",
      "28      1980     452\n",
      "29      1990     431\n",
      "30      1988     423\n",
      "31      1989     419\n",
      "32      1985     359\n",
      "33      1982     344\n",
      "34      1986     320\n",
      "35      1992     308\n",
      "36      1976     251\n",
      "37      1957     227\n",
      "38      1981     221\n",
      "39      1977     153\n",
      "40      1956     153\n",
      "41      2016     140\n",
      "42      1978     133\n",
      "43      1958     122\n",
      "44      1979     116\n",
      "45      1954     101\n"
     ]
    }
   ],
   "source": [
    "# 统计特征值出现频次大于100的特征\n",
    "for feature in categorical_feas:\n",
    "    df_value_counts = pd.DataFrame(data_train[feature].value_counts())\n",
    "    df_value_counts = df_value_counts.reset_index()\n",
    "    df_value_counts.columns = [feature, 'counts'] # change column names\n",
    "    print(df_value_counts[df_value_counts['counts'] >= 100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 简要分析\n",
    "此步骤和特征nunique分布结合步骤结合起来看，有一些小于100的是可以直接统一归类为其他的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Label分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:06:19.982362Z",
     "start_time": "2019-12-24T14:06:18.263931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x148a8d47ba8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FupQkXSwDKklSseTyZDyb2verwCMXUYdm8dMX+3nVussbPQXTAqrTA5fdl6Sy0g5snvZ+E3BirmMiogpoAs6c59z59HkpdUiSisyASpJULHk9Gb8X+HZK6TsXUYdTN2YYGZ+gvXuQK9saL7uvFTVV1FdXcqTLgErSyzwO7IiI7RFRQ2Fq954Zx+wBPpy9fh/w9Wyk7B7g9mwtw+0UpnE/Ns8+Z9oD/JNszcJbgZ6U0smFuEFJ0vwZUEmSimXRn4xHxO8BbcAnLrIOp27McKxrkMkEV65pWJD+WhpqOHbGNagkvSQbOftx4FHgWeChlNL+iLg7It6bHfZ5oDUiDlL4t/2u7Nz9wEPAMxRGzH4spTQxV58AEfFbEdFO4XvgqYj4k+waDwOHKEwn/2PgN4p865KkWVTlXYAkqWyde4oNvEDhKfYHZxwz9WT8+0x7Mh4Re4D7I+I/Aht46cl4zNVnRHwUeBfwjpTS5IxrfDwiHgDegE/G5+X5jsJopyvbDKgkFU9K6WEKAdH0tk9Nez0MvH+Oc+8B7plPn1n7Z4HPztKegI9dbO2SpIVlQCVJKoqU0nhETD3FrgS+MPVkHNiXUtpD4cn4n2ZPxs9QCJzIjpt6Mj5O9mQcYLY+s0v+J+Ao8P3COut8NaV0N4U/Un6RwpPxQeDXin/3pe9wZyGg2rZAI6haG2p49mQv4xOTVFU6gFuSJEkvV1YBVbb9+D7ghZTSe/KuR5KWu0V+Mj7rd5pPxi/N4c5+1jTWsqquekH6a2moYXwycbJnmM0tKxakT0mSJJWPcnuE+S8pzDWXJEmX4VDHwIJN74NCQAVwtMtpfpIkSXqlsgmoImIT8EvAn1zoWEmSdH6HOwcWbIF0eCmgch0qSZIkzaZsAirg/wX+N2Bytg/dPlySpPnpGRyja2B0QUdQraqvpqaygqNnBhasT0mSJJWPsliDKiLeA5xOKT0REW+b7ZiU0n3AfQC7du1Ki1ieJEkl5T9963kAjp8Z4v69xxakz4oINq2u55hT/CRJkjSLchlB9WbgvRFxBHgAeHtEfDnfkiRJKk2d/SMAtDbWLGi/W1pXOMVPkiRJsyqLgCql9MmU0qaU0jYKW5R/PaX0oZzLkiSpJHX0j1ARL60btVC2tqzgWNcghY0VJUmSpJeURUAlSZIWTmf/KKtX1FBVsbC/JmxuWUHfyDhnB8cWtF9JkiSVvrILqFJK30wpvSfvOiRJKlVd/SOsaaxd8H63thYWXT/qND9JkiTNUHYBlSRJunSTk4nO/hHWLPD6UwBbWlYAuA6VJEmSXsGASpIknXOqd5ixicSalQs/gupcQNU1sOB9S5IkqbQZUEmSpHMOdxbCo2JM8auvqWTtylqOdjmCSpIkSS9nQCVJks451NEPFCeggsIoKqf4SZIkaSYDKkmSdM6hzgFqKitYVVdVlP43t6ygvXuoKH1LkiSpdBlQSZKkcw53DrCmsYaIKEr/65vqeLF3mInJVJT+JUmSVJoMqCRJ0jmHOgZoLdL0PoD1zfWMTya6+keKdg1JkiSVHgMqSZIEwMj4BO3dg0Vbfwpg/ao6AE70DBftGpIkSSo9BlSSJAmAY12DTCZoW1lTtGusby4EVCfPug6VJEmSXmJAJUmSgMIC6VC8HfwA1jfVA3DSEVSSJEmaxoBKkiQBhQXSobgB1eoV1dRWVXCyxxFUkiRJeokBlSRJAuBQRz9rGmupq64s2jUigvVNdY6gkiRJ0ssYUEmSJKAwgurKNQ1Fv876pnoDKkmSJL2MAZUkSQKygKptEQKq5jpOGVBJkiRpGgMqSZJEz9AYnf2jbF+UEVR1nOodZmIyFf1akiRJKg0GVJIk6dwC6Ve2NRb9Wuub6pmYTHT0jRT9WpIkSSoNBlSSJInDnf0AizaCCnAnP0mSJJ1jQCVJkjh4up/KimBLy4qiX2t9Uz2AC6VLkiTpHAMqSZLEU+09XLtuJTVVxf/V4KURVAZUkiRJKjCgkiRpmZucTPz4+Fles6l5Ua7XvKKauuoKTp51ip8kSZIKqvIuQJIk5etI1wC9w+PctLlpUa4XEWxoqudkryOoJKnc9A6NcbCjn8OdA6xdWcutV7ZSXem4CEkXZkAlSdIy9+P2swDcuHlxRlABrGuqcwSVJJWZZ070cv9jR5lMUFddwRNjk/zgUBfvec0Grlu/ivv3Hrukfj/4hi0LXKmkpcgoW5KkZe7Hx3tYUVPJjrUrF+2a65vqXYNKksrIi73DPPTEcTY01/Obb7+af/NLO/lnb95OTVUFX/7BUQ6c6su7RElLnAGVJEnL3I+On+WGjU1UVsSiXXN9Ux2n+0YYn5hctGtKkopjaHSCL//gKDWVFfzKG7ayvqmeigiuXtvIv/j5q1nXVMeD+47R1T+Sd6mSljADKkmSlrHR8UmeOdHLTYs4vQ9gfXMdE5OJDv9YkaSS98j+U3QPjvLBW7bQVF/9ss9qqir40Bu2EgR/+oOjjIxP5FSlpKXOgEqSpGXsuVO9jE5McuMi7OB3/95j535+eqofgP/yvSNFv64kqXgGRsZ58lg3r9vawrY1DbMes7qhhjtu2cLpvhG+eaBjkSuUVCoMqCRJWsZ+fHxqgfTF2cFvSktDDQBnBkYX9bqSpIW178gZxicTb7yq9bzHXb22kZs3N/O9g510D/pvv6RXMqCSJGkZ+9HxHtY01rCxuX5Rr9u8oprAgEqSStnEZOIHh89wZVsD61bVXfD4v7fzCgAe3X+q2KVJKkEGVJIkLWNPHu/mNZuaiVi8BdIBqisrWFVfbUAlLXMRcVtEHIiIgxFx1yyf10bEg9nneyNi27TPPpm1H4iId12oz4jYnvXxs6zPmqx9S0R8IyKejIinIuIXi3vX5eOZk730DI3x5qvWzOv45hU1/NyONTzV3sPxM4NFrk5SqTGgkiRpmTpxdohDHQO88crzT8soltUrajjjNA9p2YqISuBzwLuBncAdEbFzxmEfAbpTSlcDnwE+nZ27E7gduB64Dbg3Iiov0Oengc+klHYA3VnfAP8GeCildHPW573FuN9y9P3nu1i9oppXrVs573PeuqONxtoq/uYZR1FJejkDKkmSlqnv/KywUO1br2nL5fqtDTV0O4JKWs5uAQ6mlA6llEaBB4DdM47ZDXwxe/0V4B1RGPK5G3ggpTSSUjoMHMz6m7XP7Jy3Z32Q9fkPstcJWJW9bgJOLPB9lqW+4TGOdA2wa1sLFRcxCre2upK3XL2G5zsGeOHsUBErlFRqDKgkSVqmvv2zTtaurOWaKxpzuf7qhhp6h8cZHnPLcWmZ2ggcn/a+PWub9ZiU0jjQA7Se59y52luBs1kfM6/1+8CHIqIdeBj4zcu5qeXipy8WdmO99iJGT025ZXsLtVUV5x6USBIYUEmStCxNTCa++7NOfm5H26KvPzVlaic/1yGRlq3Z/vFJ8zxmodoB7gD+S0ppE/CLwJ9GxCv+ToqIOyNiX0Ts6+gwWDnwYh+r6qrmtTj6THXVldyyrYWnX+hxLUJJ5xhQSZK0DP3khR56hsZ46zXzW9i2GKYCqmMGVNJy1Q5snvZ+E6+cXnfumIioojAF78x5zp2rvRNozvqYea2PAA8BpJS+D9QBr/jHMaV0X0ppV0ppV1tbPlOjl4qxiUl+9mIf11yx8pIfcrzp6sL/4u8d7FzI0iSVMAMqSZKWoe/8tPD0/y1XG1BJys3jwI5sd70aCguU75lxzB7gw9nr9wFfTymlrP32bJe/7cAO4LG5+szO+UbWB1mfX8teHwPeARAR11EIqBwidR5PHO1mZHzyohZHn6mpvpobNzWz7+gZhkad6i0Jqi58iCRJKhX37z02a/sH37DlZe+/87NObti4itbG2sUoa1YNNZXUVFVwtMuASlqOUkrjEfFx4FGgEvhCSml/RNwN7Esp7QE+T2HK3UEKI6duz87dHxEPAc8A48DHUkoTALP1mV3yXwMPRMT/DTyZ9Q3wO8AfR8T/QmHa3z/NAi3N4RsHTlMZwVVtl7eG4ZuuWsOTx8/y5PFu3nRVfg9MJC0NBlSSJC0zfcNj/PBYN3e+9cpc64gIWlbUuAaVtIyllB6msDD59LZPTXs9DLx/jnPvAe6ZT59Z+yEKu/zNbH8GePPF1r6cffO5DrauWUFddeVl9bNxdT2bVtez9/AZ3nhla25rIkpaGgyoJEkqQ8NjEzzy9ClO9Q5zZmCU5zv6+Te/dB0Rwb99+FnGJxN///p1c464WiwtDTVO8ZOkEvLC2SEOvNjHu29YtyD9vWF7C3/xwxc43DXAlWvy2VVW0tLgGlSSJJWhA6f6eOzIGQA2NNfx+e8e5t8/eoCv/rCdP3/sOL/xtqu4aXNzzlW+FFBNTjqbRpJKwd5DXQDsWHvp609N9+qNzdRVV/DY4TML0p+k0uUIKkmSylB79yBVFcGv/9yVVAQ8c7KXP/rm81RVBG/Y3sIn/t41eZcIFAKqkfFJOvpHuOIStiqXJC2uHx7rZmVtFWtXLcwahjVVFbx2y2r2HjpD36vHWFlXvSD9Sio9jqCSJKkMtZ8dYkNzPZUVQURw9+4b+Ic3b+SKVXX8wR03U1W5NH4FcCc/SSotTx47y42bm6lYwPWibtnewkRKPHns7IL1Kan0LI3fTiVJ0oKZmEycODvExub6c22VFcFnPnAT3/pXb2PtEhqpdC6gcic/SVryBkfHee5UHzdvWdgp4mtX1rG1ZQX7jp7BDRSl5cuASpJUNBFxW0QciIiDEXHXLJ/XRsSD2ed7I2LbtM8+mbUfiIh3XajPiPh41pYiYs209rdFRE9E/Cj7Obc7VLnq6BthbCKxafVLAdX9e49x/95jPLSv/dzrvBdIB2heUU1FwFFHUEnSkveT9h4mJtOCB1QAu7atprN/lKM+sJCWLQMqSVJRREQl8Dng3cBO4I6I2DnjsI8A3Smlq4HPAJ/Ozt0J3A5cD9wG3BsRlRfo83vAO4Gjs5TznZTSTdnP3Qt5n0tRe3fhl/uN0wKqpaqqooLNLSt4/nR/3qVIki7gyeOFKXg3bV694H3fsLGJmqoK9h3tXvC+JZUGAypJUrHcAhxMKR1KKY0CDwC7ZxyzG/hi9vorwDsiIrL2B1JKIymlw8DBrL85+0wpPZlSOlLsmyoF7WeHqK2qYE3jwixgW2zXXLGSAy/25V2GJOkCnjzWzbbWFeemZy+k2qpKXrOxiZ+8cJbhsYkF71/S0mdAJUkqlo3A8Wnv27O2WY9JKY0DPUDrec6dT5+zeWNE/Dgi/joirp/tgIi4MyL2RcS+jo6OeXS5dL3QXVh/aiEXsC2msYlJDnX086W/O7Kkph9Kkl6SUuKHx85y85aFHz01Zde2FsYmEj9p7ynaNSQtXQZUkqRimS0dmbny6VzHXGz7+fwQ2JpSuhH4A+CvZjsopXRfSmlXSmlXW1vbBbpcusYnJjnVM/yy9aeWuitW1jGZoLN/NO9SJElzONEzTEffSFHWn5qyeXU9a1fWsu/omaJdQ9LSVRYBVUTURcRj2dPx/RHxf+ZdkySJdmDztPebgBNzHRMRVUATcOY8586nz5dJKfWmlPqz1w8D1dMXUS83p3qHmUiJjatX5F3KvF2R7Sr4Yu9wzpVIkuby5LHC2lA3F2H9qSkRwa6tqznePeR3grQMlUVABYwAb8+ejt8E3BYRt+ZckyQtd48DOyJie0TUUFj0fM+MY/YAH85evw/4eirsL70HuD3b5W87sAN4bJ59vkxErMvWtSIibqHw3de1IHe4BLV3DwGU1AiqNY01VIQBlSQtZU8eO0ttVQXXrl9Z1OvctGU1lRHsO+IoKmm5KYuAKhVMbf9Tnf1caMqHJKmIsjWlPg48CjwLPJRS2h8Rd0fEe7PDPg+0RsRB4BPAXdm5+4GHgGeAR4CPpZQm5uoTICJ+KyLaKYyqeioi/iS7xvuApyPix8BngduzEKwsnewZpr66kub66rxLmbeqygpaG2t5sW8k71IkSXPYf6KHa9evorqyuH9CNtZWce36lTx5/Czjk5NFvZakpaUq7wIWSrb1+BPA1cDnUkp7cy5Jkpa9bErdwzPaPjXt9TDw/jnOvQe4Zz59Zu2fpRBAzWz/Q+APL7b2UtU3PEbzipTZicIAACAASURBVGqiRBZIn3LFqjpOnB3KuwxJ0ixSSjx3qo9337BuUa63a2sL+0/08uzJPl69sWlRrikpf2Uxggoge7J+E4Un57dExA3TPy+n3ZkkSZpL3/A4jbWl9/zpipW1dA+MMjru03JJWmpe7B3h7OAY165btSjX23FFI0311TzhYunSslI2AdWUlNJZ4JvAbTPay2J3JkmSzqd/ZJyVdSUYUK2qIwEdTvOTpCXn2VO9AFy7rrjrT02piOC1W5r52Yv9nB10h1dpuSiLgCoi2iKiOXtdD7wTeC7fqiRJWlwpJfpHSnME1dpVtYALpUvSUnTgVB/Aoo2gAnjd1hYS8MNs90BJ5a/0foOd3Xrgi9k6VBUUFs39bznXJEnSouodGmdiMpVkQNXaUEtlRfBinwGVJC01z53sZUNTHU0rFm8DjpaGGq5sa+CJo91MTiYqKkprbUVJF68sRlCllJ5KKd2cUnpNSumGlNLdedckSdJi6+gvTI9rrCudHfymVFYEa1fWOoJKkpag5071ce36xRs9NWXX1ha6B8f4/qGuRb+2pMVXFgGVJEl6af2mUhxBBbB2ZS2ne12DSpKWktHxSQ6e7l+09aemu37DKuqqK3jw8eOLfm1Ji8+ASpKkMtGZjaAqxUXSAdY01tIzNMbYhDv5SdJS8XxHP+OTKZcRVNWVFdy0uZlH9p+iZ3Bs0a8vaXEZUEmSVCamAqpSHUHV2lhDAroH3LFJkpaK57Id/K7LYQQVFKb5jY5P8rUfv5DL9SUtHgMqSZLKREffCBUB9TWVeZdySVobCjv5dRlQSdKS8dzJPmoqK9i+piGX629oruf6Dauc5ictAwZUkiSVic7+ERprq6iI0tzpqLWhBjCgkqSl5NlTfVy9tpGqyvz+dPzA6zez/0QvT7/Qk1sNkorPgEqSpDLR2T9astP7oDDyq666gq5+F0qXpKXiuZO9XLs+n+l9U3bfuJGaqgoe2ucoKqmcGVBJklQmOvpGaCzRBdIBIoLWhlrOOIJKkpaEnsExTveNcM0V+QZUTSuque36dfzVky8wPDaRay2SiseASpKkMlGY4leddxmXpaWhxil+krREPN/ZD8DVbY05V1KY5tc7PM6j+0/lXYqkIjGgkiSpDKSU6OwfYWUJj6CCwk5+3QOjjE9O5l2KJC17z58uBFRXrc0/oHrjla1sbqnn/r3H8i5FUpGU9m+xkiQJgJ6hMcYmUkmvQQWFnfwScHZwLO9SJGnZmCv0eeTpU1RG8N2fdfL957sWuaqXq6gIfuUNW/l3f/0c+0/0cP2GplzrkbTwHEElSVIZ6MwWFi/9gCrbya/faX6SlLeO/hFaG2uorFgau8Pe8fot1FdX8oXvHsm7FElFYEAlSVIZON2XBVRlMMUPoGvAnfwkKW8dfSO0razNu4xzmlZU88u7NvH//fgEp/uG8y5H0gIzoJIkqQx0ZiOOSn0EVWNtFTWVFS6ULkk5m5hMnBkYoa1x6QRUAL/25u2MTU7y5e8fzbsUSQvMgEqSpDLQmY2gKvVF0iOC1sYazjjFT5Jy1TUwwmRiSY2gAti2poF3XHsFX957jOGxibzLkbSADKgkSSoDHf0jVFcG9dWVeZdy2VoaahxBJUk5m3rwsdQCKoA733olZwZG3dFPKjMGVJIklYHOvhFaG2qJWBoL2V6O1oZaugdGmZhMeZciSctWRxZQrVliU/wAbtnewhuvbOWPvvW8o6ikMmJAJUlSGejsX1oL2V6O1sYaJlLixNmhvEuRpGWro3+EVXVV1C3Rkbn/8p076OgbcRSVVEYMqCRJKgMd/SOsyXbAK3WtDYX7ONo1mHMlkrR8dfSNsGYJP/i49cpWR1FJZcaASpKkMtDZN1pGI6gK93GkayDnSiRpeUop0dG/9Hbwm2lqFNWXvn8k71IkLQADKkmSSlxKiTMDo7Q0LO0/JOZrZV0VVRXBUQMqqexFxG0RcSAiDkbEXbN8XhsRD2af742IbdM++2TWfiAi3nWhPiNie9bHz7I+a6Z99ssR8UxE7I+I+4t3x6Whf2Sc4bHJJf/g49YrW3nbq9r4g/95kM7+kbzLkXSZDKgkSSpxQ2MTjE5M0ryiOu9SFkRFBC0NNRxxip9U1iKiEvgc8G5gJ3BHROyccdhHgO6U0tXAZ4BPZ+fuBG4HrgduA+6NiMoL9Plp4DMppR1Ad9Y3EbED+CTw5pTS9cBvF+mWS0ZHFvYs9RFUAL/7np0MjU3w/zx6IO9SJF2mqrwLkCRJl6dnaAyApvpqUplsfNfaWOsIKqn83QIcTCkdAoiIB4DdwDPTjtkN/H72+ivAH0Zhu9LdwAMppRHgcEQczPpjtj4j4lng7cAHs2O+mPX7R8CvA59LKXUDpJROL/ytlpauvlFgae7gN9NVbY380zdt4/PfO8yHbt3KDRubLrmvS1lw/YNv2HLJ15P0co6gkiSpxE0PqMpFa0MNR7sGmZwsk8RN0mw2AsenvW/P2mY9JqU0DvQArec5d672VuBs1sfMa10DXBMR34uIH0TEbZd5XyWva2CEyoqgqURG5v7WO3fQsqKG3/3a00z4vSGVLAMqSZJKXM9gGQZUjTWMjE/yn799iPv3Hjv3I6msxCxtM9OFuY5ZqHYozCrZAbwNuAP4k4hofkWxEXdGxL6I2NfR0TFLd+Wja2CUlhU1VMRs/9uWnlV11fzue3by5LGz/PF3DuVdjqRLZEAlSVKJK88RVIVpJV0ueiuVs3Zg87T3m4ATcx0TEVVAE3DmPOfO1d4JNGd9zLxWO/C1lNJYSukwcIBCYPUyKaX7Ukq7Ukq72traLvJWS0tX/yitjTUXPnAJ2X3TBm67fh3/8W9+ynOnevMuR9IlMKCSJKnElWNA1dJQ+MOoa2A050okFdHjwI5sd70aCoue75lxzB7gw9nr9wFfTymlrP32bJe/7RQCpcfm6jM75xtZH2R9fi17/VfALwBExBoKU/6W7TCclBJdAyO0NpRWQBUR3PMPb2BVfRWfePDHjIxP5F2SpItkQCVJUombCqhW1ZVPQNVUX01lBF39BlRSucrWg/o48CjwLPBQSml/RNwdEe/NDvs80Jotgv4J4K7s3P3AQxQWVH8E+FhKaWKuPrO+/jXwiayv1qxvsmO7IuIZCiHWv0opdRXz3peyvpFxxiYSLSWwQPpMrY21/Lt/9BqeOdnL7+955sInSFpS3MVPkqQS1zs0RgSsrCufr/XKimB1QzVdA07xk8pZSulh4OEZbZ+a9noYeP8c594D3DOfPrP2Q7y009/09kQh/PrERZZfls5kDwZKbQTVlHfuvILfeNtV3PvN53n1xiZ32ZNKiCOoJEkqcT1DY6ysraKiojQWs52v1oZazjjFT5IW1dTU6lINqAB+5++/ip+/po3f2/M0+46cybscSfNkQCVJUonrGRorma3AL0ZLYw1dA6MUBjdIkhZD18AIFQHNK0o3oKqsCD57+81sWr2Cj35pHwdP9+VdkqR5MKCSJKnE9QyNldUC6VNaG2oYHZ+kf2Q871Ikadno6h+leUUNlSU+KrdpRTVf/LVbqKqo4MNfeJxTPcN5lyTpAgyoJEkqceUbUBUW6HWanyQtnjMDoyU9vW+6La0r+C+/9np6hsb48BceO7epiKSlyYBKkqQSV7YBVWPhDyR38pOkxZFSomtg5Ny/v+Xgho1N/OdffR2HOvv59S/tY3hsIu+SJM3BgEqSpBLXMzRelgFV84pqKgI63clPkhbF4OgEw2OT50awlos3X72G//DLN/HY4TP89gM/YmLStQ2lpciASpKkEpZSondojFVlGFBVVVSwekUNnX0GVJK0GMphB7+5vPfGDXzqPTt5ZP8pfm/P027AIS1BVXkXIEmSLt3w2CSjE5NlOYIKYO3KWk4bUEnSojiTjVhtKaMpftP9s7ds58W+Yf7ztw5xxco6fvMdO/IuSdI0BlSSJJWwqQVfyzagWlXHgRf7GJ+cpKrCgd+SVExd/aME0LKiPAMqgLtuu5aOvhH+w9/+lHVNdbx/1+a8S5KUMaCSJKlE3b/3GKd6C9tm/6S9h/s5lnNFC2/tylomU+GPpitW1eVdjiSVta6BUZrqq6mqLN8HAhHBp//xazjdO8L/8VdPc+26Vbx6U1PeZUnCNagkSSppQ6OF3YjqaypzrqQ4pkIpp/lJUvF19Y+U7fS+6aorK/jsHTfT1ljLP//yE3QPuFustBQYUEmSVMKmtsuury7PgGpNYy0BnM5GikmSiqdrYLTsdvCbS0tDDff+ymvp6Bvhtx/8kYumS0uAAZUkSSXs3AiqMg2oaqoqWN1Q4wgqSSqyodEJBkcnynIHv7ncuLmZ333PdXzrpx3c/1j5TZOXSo0BlSRJJWxorLyn+EFhHaoXHUElSUV1Jpvm1roMpvhN96Fbt/KWq9fwb//7s071k3JmQCVJUgmbCqjqynQEFcDalXV09Y8yMen0C0kqlq6BwkjV5TLFb0pE8O/+8auJCP7iyXYmneon5caASpKkEjY0OkFddQUVEXmXUjRXrKplIqVzfzxJkhbe1AiqlmU0xW/KptUr+N9/8ToOdQzw5LGzeZcjLVsGVJKkoomI2yLiQEQcjIi7Zvm8NiIezD7fGxHbpn32yaz9QES860J9RsTHs7YUEWumtUdEfDb77KmIeG3x7njxDY1NlO36U1PWrsx28us1oJKkYunqH2VVXRU1VcvzT8TbX7+Zzavr+Zv9pxgZn8i7HGlZWp7/+kiSii4iKoHPAe8GdgJ3RMTOGYd9BOhOKV0NfAb4dHbuTuB24HrgNuDeiKi8QJ/fA94JHJ1xjXcDO7KfO4E/Wsj7zNvQaPkHVG0rs538+lyHSpKKpWtgZFmOnppSURH80ms20DcyzrcOdORdjrQsVeVdgCSpbN0CHEwpHQKIiAeA3cAz047ZDfx+9vorwB9GRGTtD6SURoDDEXEw64+5+kwpPZm1zaxjN/ClVNg/+gcR0RwR61NKJxf0bnMyNDZBXRkvkA6FnfyaV1TzoiOoJKlougZGuWbtyrzLWDD37734Xfm2tKzgps3NfPdgJ7u2tSzrwE7KgyOoJEnFshE4Pu19e9Y26zEppXGgB2g9z7nz6fNS6iAi7oyIfRGxr6OjdJ6cLocpflCY5tfRZ0AlScUwOj5J3/D4stvBbzbvun4dEfC3z5zKuxRp2SmLgCoiNkfENyLi2YjYHxH/Mu+aJEnMtmr3zK1x5jrmYtsvtw5SSvellHallHa1tbVdoMulY3gZTPEDuGJVIaAaHZ/MuxRJKjtTm1A4Ygia6qt545WtPNXe49RyaZGVRUAFjAO/k1K6DrgV+Ngs65xIkhZXO7B52vtNwIm5jomIKqAJOHOec+fT56XUUbKGxiaoL/MpfgAbmuuYSImfne7LuxRJKjtd/YUd/Foba3OuZGl4y442qiqDbzx3Ou9SpGWlLAKqlNLJlNIPs9d9wLNceMqHJKm4Hgd2RMT2iKihsOj5nhnH7AE+nL1+H/D1bK2oPcDt2S5/2ykscP7YPPucaQ/wT7Ld/G4Fespl/amxiUnGJ9OyGEG1oakegP0v9OZciSSVnzMDWUDlCCoAGmuruNVRVNKiK4uAarpsi/Kbgb0z2ktybRFJKlXZmlIfBx6l8ODgoZTS/oi4OyLemx32eaA1WwT9E8Bd2bn7gYcoLKj+CPCxlNLEXH0CRMRvRUQ7hRFST0XEn2TXeBg4BBwE/hj4jSLf+qIZGi1sg70cRlC1NNZQU1XB/hM9eZciSWWna2CUhppK6pbBA4/5+rlsFNU33dFPWjRltYtfRDQCfwH8dkrpZY9YU0r3AfcB7Nq160LrlUiSFkBK6WEKAdH0tk9Nez0MvH+Oc+8B7plPn1n7Z4HPztKegI9dbO2lYGgsC6iWwR8UFRGsX1XH/hOOoJKkhdY1MLKkp/ddyo58l6uxtopbt7fy3YOdvPO6K1yfS1oEZTOCKiKqKYRTf5ZS+mre9UiSVGznRlAtg4AKYENzPc+e7GVy0udMkrSQzvSPOr1vFm+6eg0R8P3nO/MuRVoWyiKgioigME3k2ZTSf8y7HkmSFsO5EVTLYIofFBZKHxid4EjXQN6lSFLZGB6boGdozBFCs2iqr+Y1m5p5/Gg3w9l3rqTiKYuACngz8KvA2yPiR9nPL+ZdlCRJxbScpvgBrJ9aKN1pfpK0YNq7B0lAa6MB1WzefPUaRscnefzImbxLkcpeWQRUKaXvppQipfSalNJN2c8r1ieRJKmcLLcpfmtX1VJdGTztQumStGCOdA4C0NqwdNegytPG5nq2r2ng757vYsIp5lJRlUVAJUnScjQ1gqpumUzxq6qo4JorVvKMI6gkacFMTZt2Daq5veXqNfQMjfHMSb9/pGIyoJIkqUQNjU1QW1VBRUTepSyaGzY0sf9EL4XNGSVJl+to1yB11RXLZj3DS/GqdStpXlHN3sNdeZcilTUDKkmSStTw6MSy+4Pi+o2rODMwyqne4bxLkaSycKRrgNaGWmIZPey4WBURvH5bC4c6BujsH8m7HKlsGVBJklSihsYmls36U1Nu2NgEwJPHzuZciSSVh6Ndgy6QPg+v27qaioDHD7tYulQsBlSSJJWoodHlF1C9emMTq+qq+MZzp/MuRZJK3uj4JO3dg64/NQ+r6qq5bv0qnjjWzfjEZN7lSGWpKu8CJEnSpRkam6Bt5fLadam6soK3XtPGNw50MDmZqKgoTEm5f++xVxz7wTdsWezyJKmkvHB2iMnkDn7zdcv2Fvaf6GX/iV5u3NycdzlS2XEElSRJJWo5TvEDeMd1a+nsH+EnL/TkXYoklbRzO/g5xW9ermprpKWhhseOOM1PKgYDKkmSStRynOIH8PPXrCUCvu40P0m6LEc7CwFVi1P85mVqsfTDnQOcdrMOacEZUEmSVIKGxyYYn0zLbhc/KPwh9dotq/nGAQMqSbocR7oGaaippLHWlV/m63VbV1MZweOOopIWnAGVJEklqHdoDIC6ZTiCCuDt167lqfYen2BLJS4ibouIAxFxMCLumuXz2oh4MPt8b0Rsm/bZJ7P2AxHxrgv1GRHbsz5+lvVZM+Na74uIFBG7inO3S8/RrgG2tjYQEXmXUjIaa6vYuWEVPzx2ljEXS5cWlAGVJEklqCcLqJbjCCooBFQA3zzQkXMlki5VRFQCnwPeDewE7oiInTMO+wjQnVK6GvgM8Ons3J3A7cD1wG3AvRFReYE+Pw18JqW0A+jO+p6qZSXwW8DeYtzrUnW0a5Bta1bkXUbJuWV7C0NjEzztWojSgjKgkiSpBJ0LqJbpCKpr161kY3M9X/vxC3mXIunS3QIcTCkdSimNAg8Au2ccsxv4Yvb6K8A7ojDcZzfwQEppJKV0GDiY9Tdrn9k5b8/6IOvzH0y7zv8F/Htg2QzLHJ+Y5Hj3IFtbG/IupeRcuaaB1oYa9h52mp+0kAyoJEkqQcs9oIoIPnTrVr53sMsn2FLp2ggcn/a+PWub9ZiU0jjQA7Se59y52luBs1kfL7tWRNwMbE4p/bfLv6XScbJnmLGJxLZWR1BdrIjglu0tHDszyE9f7Mu7HKlsGFBJklSClvsUP4APvmELjbVV3PftQ3mXIunSzLbwUZrnMQvSHhEVFKYO/s556iwUEnFnROyLiH0dHaU/vfhIV2EHP0dQXZqbtxQWS3/gseMXPljSvBhQSZJUgpb7CCqApvpq7rhlM//9JyfpHhjNuxxJF68d2Dzt/SbgxFzHREQV0AScOc+5c7V3As1ZH9PbVwI3AN+MiCPArcCe2RZKTyndl1LalVLa1dbWdtE3u9Qc6RoEYJsB1SVprK3iug2r+OqT7YyMT+RdjlQWDKgkSSpBPct8F78p/+wt2wngu8935l2KpIv3OLAj212vhsKi53tmHLMH+HD2+n3A11NKKWu/PdvlbzuwA3hsrj6zc76R9UHW59dSSj0ppTUppW0ppW3AD4D3ppT2Feuml4qjnQPUVVewdmVt3qWUrNdvW83ZwTEe3f9i3qVIZcGASpKkEtQzNEZtVQWVFct7a/D1TfW896YN7DtyhpExn2BLpSRbD+rjwKPAs8BDKaX9EXF3RLw3O+zzQGtEHAQ+AdyVnbsfeAh4BngE+FhKaWKuPrO+/jXwiayv1qzvZetI1yBbWxqoWObfI5fjqrZGNq2u54HHjuVdilQWqi58iCRJWmp6hsaW9fS+6T54yxa++sMXeOZkLzdvWZ13OZIuQkrpYeDhGW2fmvZ6GHj/HOfeA9wznz6z9kMUdvk7Xz1vm0/d5eBo1wDb1zi973JURPCBXZv5D3/7U452Dbiel3SZHEElSVIJ6h0aW9YLpE/3uq2rWb2imh8dP5t3KZJUEiYnE0fPDLLNgOqyvX/XZioCHnzcxdKly2VAJUlSCeoZGlv2609NiQhu3NzMwdP99A2P5V2OJC15L/YNMzo+ydbWFXmXUvLWNdXx9mvX8l+faGd8YjLvcqSSZkAlSVIJcorfy920qZkEPNXek3cpkrTkHel0B7+F9IHXb6Gjb4SvP3c671KkkmZAJUlSCepxit/LrF1Vx4bmOqf5SdI8HO0aAHAE1QL5hVe1ccWqWh5wmp90WQyoJEkqQY6geqWbNjXzwtkhOvpG8i5Fkpa0I12D1FRWsL6pPu9SykJVZQXvf91mvnngNCd7hvIuRypZBlSSJJWYkfEJhscmHUE1w2s2NRPAT15wmp8knc/RrgE2t9RTWRF5l1I2PvD6zUwm+K/72vMuRSpZBlSSJJWYnqHCQuCOoHq5VfXVbG5Zwf4TBlSSdD5HugZdf2qBbW5Zwc/tWMMDjx1zsXTpEhlQSZJUYnoNqOZ0w4ZVnOwZ5szAaN6lSNKSlFLiaNcAW1x/asF96NatnOgZ5n+6WLp0SQyoJEkqMedGUDnF7xV2bmgCcBSVJM2ho3+EwdEJR1AVwTuuXcuGpjq+9P0jeZcilSQDKkmSSoxT/ObW0lDDhuY69p/ozbsUSVqSjnYNAu7gVwxVlRX8yq1b+d7BLg6e7s+7HKnkGFBJklRiDKjO7/oNTRw7M8ipnuG8S5GkJedI5wCAI6iK5AOv30x1ZfDlHxzNuxSp5BhQSZJUYnoGCwFVnVP8ZnX9hlUAPLr/VM6VSNLSc7RrkMqKYOPq+rxLKUtrGmv5xVev5y+eaKd/ZDzvcqSSYkAlSVKJ6Rkq/MLrCKrZrV1ZR9vKWh552oBKkmY60jXAptX1VFf6p2Cx/Nqbt9M3Ms6Djx/PuxSppPivkiRJJaZnaIyGmkoqKyLvUpasGzasYu/hLrr6R/IuRZKWlMOdA07vK7KbNjfz+m2r+cJ3DzM+MZl3OVLJMKCSJKnE9A6P0VRfnXcZS9r1G5qYTPA/nn0x71IkacmYnEwc6hjgqrbGvEspe7/+c1fywtkh/trRvNK8GVBJklRiugdGaVpRk3cZS9r6pjo2t9T7h4EkTXOyd5ihsQmuWusIqmJ753VXsH1NA3/8nUOklPIuRyoJVXkXIEn/P3t3Hh9XXe9//PVJJvvarG2TLmmbtpTSFlpaNoGyCLJYflK0gCjae7kguFz0qqjXy+XqvaL3J6IgiIgCCmVVKr8iIBQK2B26r+mebtmaNluzfn9/zGlJ00mapknOTPJ+Ph7zyMl3zvnO53znzJyZz3y/3yMiJ6espoGs5P6ZoHpm8c5OrWdmfGr8IH7/wTYOHW4kNV49zkREtpRUA6gHVS+IijJmX1DAD/6yhsXbKjhnRKbfIYmEPfWgEhERiTDl1fVkJ8f5HUbYu+L0gTQ2O95eX+J3KCIiYWFLqRJUvWnm5HyykmP51dub/Q5FJCIoQSUiIhJBnHOUVdeT2U97UJ2MM4ekk5sax2tr9vodiohIWNhSWk1qfKDf9sLtbfEx0dx+0Ug+KCpnybYKv8MRCXtKUImIiESQ2oZmDje2kKkeVCcUFWVccfpA3t1UyuHGZr/DERHx3dbSGkbmJGOmq8D2lpunDSMrOZYH39rkdygiYU8JKhERkQhSXt0AQGaSfv3ujEvG5nC4sUW/XIuIEOxBNSJLw/t6U0KselGJdJYSVCIiIhGkrKYegKwU9aDqjGkFmcRGR/He5lK/QxER8VXV4Ub2H6rXFfx8cKQX1c/f3Kgr+ol0QAkqERGRCFJW5SWokpSg6oyE2GimDB/Ae5vL/A5FRMRXW0trAE2Q7oeE2Gi+ekkhi7ZW8JYu3CHSLiWoREREIkh5jTfETxPcntAzi3fyzOKdpMTHsGFfFY++u4VnFu/0OywREV/oCn7+umnaUEZmJ/Hf89bT0NTidzgiYUkJKhERkQhSXh3sQaUEVecV5gS/jG0pqfY5EhER/2wprSYQZQzLTPQ7lH4pJjqK7199GlvLavjT4h1+hyMSlpSgEhERiSBl1Q2kxAeIC0T7HUrEGJgWT1JsNEVKUIlIP7alpIahmYnEROsroF+mj8nhglFZ/OLvm6nwekSLyMf07iQiIhJByqrryUrW/FMnI8qMkTnJbC6p1uS0ItJvbS2r1vA+n5kZ/37NOGrqm/jRq+v8Dkck7AT8DkBERPouM7sSeBCIBh53zv2kzf1xwFPAZKAc+Jxzbrt33z3AbKAZ+Jpz7vWO6jSzAmAOkAF8CNzinGsws1uBnwG7vYd9yDn3eE/tc08rr24gM0nD+05WYU4Kq4oPsu/QYb9DERHpdU3NLWwvq2X62By/Q+lzujK34ScKs3n5o93MODOPi0Zn90BUIpFJPahERKRHmFk08DDwKWAccKOZjWuz2mzggHNuFPAAcL+37ThgFnA6cCXwazOLPkGd9wMPOOcKgQNe3Uc855yb5N0iNjkFUF6jHlRdcWQeqk37qnyORESk9+2oqKWhuYVR6kEVFqaPyWZkdhLfe3k1NfVNfocjEjb6RILKzJ4wsxIzW+N3LCIictRUoMg5t9U510Cwd9OMNuvMaPLomwAAIABJREFUAJ70ll8ELjUz88rnOOfqnXPbgCKvvpB1ettc4tWBV+d1PbhvvimrbtAE6V2QmhBDXnoC6/Ye8jsUEZFet9FLzo8dmOpzJAIQiI7iJ9dPYHdlHf89b73f4YiEjT6RoAL+QPAXdhERCR95wK5W/xd7ZSHXcc41AQeBzA62ba88E6j06gj1WNeb2Soze9HMhpzKTvmpqbmFA7UNZKoHVZeMG5zKrgN1lGiYn4j0Mxv3VRFlUJirHlTh4uzhGfzLhSP40+KdvLZ6r9/hiISFPpGgcs4tACr8jkNERI5hIcrazlDd3jrdVQ7wV2C4c24C8Hc+7rF1bCBmt5nZMjNbVlpaGmoV3x2obcQ5yFYPqi4ZNyjYc+DN9ft9jkREpHdt3FfF8Mwk4mN0Bdhw8s1PjmHikHS+/dIqdlXU+h2OiO/6RIJKRETCUjHQurdSPrCnvXXMLACkEfzBob1t2ysvA9K9Oo55LOdcuXOu3iv/LcEJ2Y/jnHvMOTfFOTclOzs8Jywtqw7uhnpQdU1OShyZSbG8sVYJKhHpXzbur2J0borfYUgbsYEofjXrTHBw17Mfcbix2e+QRHzVbxJUkfDLuIhIH7MUKDSzAjOLJTjp+dw268wFvugtzwTeds45r3yWmcV5V+crBJa0V6e3zXyvDrw6XwEws0GtHu/TQMRO9lBe3QCgq/h1kZlx2qBU/rGljKrDjX6HIyLSK+oamtleXsOYgUpQhaOhmYn87IaJrNxVyXdeWkXwI41I/9RvElSR8Mu4iEhf4s0HdRfwOsGk0PPOubVmdp+Zfdpb7XdAppkVAXcD3/W2XQs8D6wD/gbc6Zxrbq9Or67vAHd7dWV6dQN8zczWmtlK4GvArT253z2pvEY9qE7VuEGpNDY73tmoH6tEpH/YXFKFczBWCaqwdeX4gfzbFWN4ZcUeHp5f5Hc4Ir4JnHgVERGRrnHOzQPmtSn7Yavlw8AN7Wz7Y+DHnanTK99K8Cp/bcvvAe452djDUZnXgypbCaouG5qZSGZSLH9bs49rJw72OxwRkR535Ap+6kEV3r5y8Ug276/if9/YxOD0BD5zVr7fIYn0uj6RoDKzZ4GLgSwzKwb+wzn3u463EhERiSxl1fUEoozUhD5x+vZFlBkzJuXx1MLt7KmsY3B6gt8hiYj0qI37qoiPiWJYZpLfoUgrzyzeeVzZmUMHsGr3Qb71wko+3FHJuMGpx9x/07ShvRWeiC/6xBA/59yNzrlBzrkY51y+klMiItIXlVfXk5kci1moixZKZ2UmxdLiHN95cRXPLN4Z8kuCiEhfsXF/FYU5KURH6dwR7mKio7hl2jAGpyfw7NKdbC6p8jskkV7VJxJUIiIi/UF5dQNZGt53ygYkxTI+L40l2yt0xSQR6fM27NMV/CJJXEw0t543nOzkOJ5euION+w75HZJIr1GCSkREJEKUVddrgvRucsGoLOqbWli2vcLvUET6NTO70sw2mlmRmX03xP1xZvacd/9iMxve6r57vPKNZnbFier0rgC72Mw2e3XGeuV3m9k6M1tlZm+Z2bCe3eveU1HTQGlVvSZIjzCJsQH+6YICclLj+OOinazbc9DvkER6hRJUIiIiEaKsuoGspFi/w+gT8gckUpCVxAdbymlqbjk61K/tTUR6jplFAw8DnwLGATea2bg2q80GDjjnRgEPAPd7244DZgGnA1cCvzaz6BPUeT/wgHOuEDjg1Q3wETDFOTcBeBH4aU/srx80QXrkSowLMPv8EQxOj+dPi3eycGu53yGJ9DglqERERCKAc47ymuAcVNI9po/J4WBdI+9sKvU7FJH+aipQ5Jzb6pxrAOYAM9qsMwN40lt+EbjUghPxzQDmOOfqnXPbgCKvvpB1ettc4tWBV+d1AM65+c65Wq98EdBnLp+2wRseph5UkSkhNprZF4xgzMAU/rpyD//16jqaW5zfYYn0GCWoREREIsChuiYON7aQmxrvdyh9xqicZCbmp/HuxlL2Hzrsdzgi/VEesKvV/8VeWch1nHNNwEEgs4Nt2yvPBCq9Otp7LAj2qnotVLBmdpuZLTOzZaWlkZHYXl18kJyUOHJ07ohYsYEoPn/OMM4dmcnv3t/GV/60nLoGzZ8ofZMSVCIiIhGguDL4435eeoLPkfQtV08YTGwgij9/tJsWp1+lRXpZqMvKtX0htrdOd5V//EBmnwemAD8LsS7Oucecc1Occ1Oys7NDrRJ2Vu0+yIT8NL/DkFMUZca1EwbzH9eO4411+5n12EJKqvTDivQ9SlCJiIhEgN0H6gAYrARVt0qOC3D1hEHsrKjltdV7laQS6V3FwJBW/+cDe9pbx8wCQBpQ0cG27ZWXAeleHcc9lpldBnwf+LRzrv6U9ipMVNc3saW0mgn56X6HIt3kS+cX8NgtU9i0v5qrf/k+izQvlfQxSlCJiIhEgN2VwQRV3gAlqLrbmUPSOWdEJh9sKeeZxTtpaGrxOySR/mIpUOhdXS+W4KTnc9usMxf4orc8E3jbOee88lneVf4KgEJgSXt1etvM9+rAq/MVADM7E/gNweRUSQ/ta69bs/sgzsEZ6kHVp1w+Lpc/33keKfEBbvrtIh6eX0SL5qWSPkIJKhERkQiw+0Ad8TFRZOoqft3OzLh2wiCumTCI9XsP8ci7ReyqqD3xhiJySrz5oO4CXgfWA88759aa2X1m9mlvtd8BmWZWBNwNfNfbdi3wPLAO+Btwp3Ouub06vbq+A9zt1ZXp1Q3BIX3JwAtmtsLM2ibJItLq4oMATMhTgqqvGTswlbl3XcDVEwbzs9c38qU/LKWipsHvsEROWeDEq4iIiIifnvEuL50cF8OzS3adeAM5aWbGeSOzyEqO4+UPi3n03S2cNzKT6yfnEReIPm79ZxbvPK7spmlDeyNUkT7FOTcPmNem7Ietlg8DN7Sz7Y+BH3emTq98K8Gr/LUtv+ykA48AK4sryUtPIDM5zu9QpAckxwX45axJTCvI4L6/ruPqX77H/94wkfNHZfkdmkiXqQeViIhIBKisbWRAYozfYfR5o3NT+MZlo5lakMEHW8qZ9dgiSnSFPxGJQKs1QXqfZ2Z8/pxhvPyV80iIiebmxxfzg7+sprq+6cQbi4Qh9aASERGJAJV1jQxO12XCe0N8TDQzJuUxIjuZv3y0m2sfep+fXD+Bi0dnYxbqQmAiIuGlsraBHeW1zDpbPTv7g/F5acz7+if439c38rsPtvHW+hJ+eM04rhw/sEvnrVC9hE9EvYiDutJ2oPY7Qj2oREREwlxjcws19U2kJ2r+qd50Rl4aL90R/FX6S79fymd/s5DlOyr8DktE5IRW7/bmn1IPqn4jPiaaH1wzjhdvP4/0xFju+NOHfOGJJazbc8jv0EQ6TQkqERGRMFdZ2whAeoKG+PW2cYNTeeNfL+K/rhvPjvJaZj66kB+9uo7GZl3pT0TC1ypvgvTxmiC935k8bAB/vet8fnjNOFYVH+TqX73HN+Z8RFFJtd+hiZyQhviJiIiEucra4JV51IPKH7GBKG45ZxjXn5XH/8zbwOPvbyMnJY4vnV9AmpKGIhKGVhVXUpCVpPeofioQHcWXLyjg+rPyeXTBFn7/wTZeWbmHy0/L5csXFDCtIEND1iUsqQeViIhImDvag0qTpPsqMTbAf103nie/PJXKukaeXridhib1pBKR8OKcY/mOA5w5JN3vUMRnaYkxfOfKsbz/nUu4a/ooFm+rYNZji5j+v+/w8Pwi9h6s8ztEkWMoQSUiIhLmDtQ1EGWQGq8EVTi4aHQ2s84ewt6Dh3lu2S5anPM7JBGRo7aUVlNW3cC0ERl+hyJhIis5jm9+cgwL77mEn392IgPT4vnZ6xs5/ydv88UnlvDKit3UNujKf+I/DfETEREJc5W1jaTGxxAdpe744WLswFSunjCIV1ftZe7KPXx64mC/QxIRAWDR1uDFHM4ZkelzJNLdTvXqeomxAT5zVj6fOSufneW1vLh8Fy99uJuvz1lBQkw0nzw9lxmTBtPc4vSZQ3yhBJWIiEiYq6xt0PC+MHTuiEwO1TWxYHMpLS2OG6cO1Qd6EfHdoq3lDEyNZ2hGot+hSBgbmpnI3Z8cwzcuG82yHQf4y4rdzFu9l1dW7CExNpoz8tKYmJ/OsMxEzVclvUYJKhERkTBXWdvI8Kwkv8OQNsyMK07PJTrKmL+xhLufX8FPZ04gLhDtd2gi0k8551i8rYLzR2YqqSCdEhVlTC3IYGpBBvdeezrvbS7lwbc28+HOAyzeVkFOShznjszkzCEDiA1ohiDpWUpQiYiIhLGm5hYOHW5UD6owZWZcPi6XmGjjlRV72Ft5mEdvmUxGkq64KCK9b2tZDaVV9UzT8D7pgthAFJeelsv+Q/XUNzWzZvchFm4t45UVe3hz3X4uLMxm2ogM/RAjPUYJKhERkTC2v6qeFgcDEpTwCGcXj8nhmomD+bcXVjLj4fd54otnU5ibAoSeM6T1nCAiIt1lseafkm4SF4hm8rABnDU0nZ0VtczfWMLf1u5jweZSLjstl7OHZ2hYu3Q79dETEREJY7sPBC8BrR5U4e/TEwcz57ZzONzYwmd+/Q/e2Vjid0gi0s8s2lpOTkocwzM1/5R0DzNjWGYSt55XwB0XjSQ3NZ65K/fw0PzNbCur8Ts86WPUg0pERCSM7ayoBSA9UT2owt2RnlJfOm84Ty/awZd+v5TLx+XyicJs/cosIj3OOceireWcM0LzT0nPGJKRyD9dUMDaPYeYt2Yvv31vK9MKMrji9IFdusIg9F6P4lO9AqL0DvWgEhERCWOb9lcRiDLNaRRB0hNjue3CEYzPS+ONdft55J0i9lTW+R2WiPRxW0qrKamqZ9qIDL9DkT7MzBifl8Y3Lh3N+SMzWbKtgl++vZkd5epNJadOCSoREZEwtmFfFdkpceqBE2HiAtHcOHUoN00dyqHDTTz67hY27Dvkd1gi0oe9sW4/AJeMzfE5EukPYgNRXD1hMP9y0UiizPjte1uZv7GEFuf8Dk0imIb4iYiIhLGN+w4xOC3B7zCki8bnpTE8K4kn/7GdPy7awQ1ThjAxP93vsESkD3pj7X4m5KcxSOcMaaWrQ+86a2hGIndNH8VfVuzmzXX72VJSzQ1ThpCWoLkz5eQpQSUiIhKmKmsb2H+onjOHDPA7lH6rOz7YJ8cFmH1BAU8t3MHzS3cRG31yHdjbi0FzY4jIESWHDrNiVyXf+uRov0ORfig+JprPTRlCYU4Kc1fu5ldvb2bm5HzGDkz1OzSJMBriJyIiEqY27KsCYGBavM+RyKmKj4nm1vOGMyg9nheW72KXN/m9iEh3eHN9cHjf5eMG+hyJ9FdmxuRhA7hreiFpCTE8tXAHf1uzl+YWDfmTzlOCSkREJExt9BJUualKUPUFsYEobpo6DIA7n/mQ+qZmnyMSkb7ijbX7GZaZyOjcZL9DkX4uOyWO2y8aydSCDBZsLuO3722lsrbB77AkQihBJSIiEqY27DtEWkIMqfEakd9XZCTFMvOsfFYVH+RbL6yioanF75BEJMJVHW5k4ZZyPjkuFzNdUEP8FxMdxXWT8vjc2UPYd+gwv3q7iI39/EIh9U3NbCurYfmOCv6xpYz3NpeysriSPZV1NLXos8AR+sQrIiISpjbsq2LMwBR94ehjxg1O44rTB/LXlXtYt+cgN08bRnxMNPDxvFKriw/y6LtbKCqpZnxeGmcOTSdKx4GIhPDOxlIamls0vE/CzsT8dPLSEnh26U6eXLiDCwuzuXxcbr+5MnFlbQOrdx9kVfFB9lTW0d5gx/iYKNbvrWLm5HwmD+vf844qQSUiIhKGWlocm/ZVcf3kfL9DkR5w0ehsUuIDvPxhMb98ezPjB6cxNCORX/x9E+9uKuWjnZWkxAfIS0/gpQ+L+ceWMm6eNoyMpFi/QxeRMPPi8mJyU+P6/RdbCU9Z3pC//7dqLws2l7KjvIbPnT2E9MTeOZ81NLWw/9BhdlXUUtfYTCDKCERHkRofIDUhptt//Kmpb2LNnoOs3HWQ7eU1AOSlJzB9bA756QnkpMYTF4giOsqorG2kpOowG/ZV8ZePdvPskp1cMjaH7101llE5Kd0aV6RQgkpERCQM7a6so6ahmTED++cHlP7grKEDSEuI4Z2NJSzcWs77RWWYwYS8NO751FhumjaUpNgA97y8mldW7ubF5cX80ycK1JNKRI7aVVHLgs2lfO2Swn7TK0UiT0x0FNedmUdBVhJ/XrGbB9/azBWnD2RqQUa3P1ZtQxPvby5jweZSVu46yIZ9h2hsDt13KTrKyEyKZXB6AoPS4hmcnsDgtAQSYqNP6jEP1DTw9oYSXl21h3c3ldLiIDs5jstOy2FCfjpZyXEhtxuYFs3AtHgm5KczY9Jg/rhoBw/NL+KKX7zHHReN5OuXFRJzklf+jXRKUImIiIShI1fwGzswhY37qn2ORnrKyOxkRmYn09DUwt6Dddxx8cjjflWeOCSdhuYW/vzRbj7ccYApw7v/A72IRKZnl+zEgFlTh/gdisgJTRySzpCMRP7y0W7mrtzDil2VnDYohcnDTu28VtvQxN/XlzB3xR4WbC6loamFpNhoJg1NZ/YFIxiRncTa3QeJj4mmucXR2NzCwbomKmrqKamqZ2tpNSt2VR6tLz0xhsFpCZRW1XP64FTGDEwhJT5AbCCK6vomyqsb2F5Ww6rdB1m+/QDLdlTQ4mBwWjwXjMpiQn46g9LiT2qKhqS4AP9y0UhumDKE/5m3nofmF/F+URkPzprEsMykU2qfSKIElYiISBg6Mpno6FwlqPqD2EAUwzKT2h3yMHnYAD7aWcm8NXu9D8oxvRyhiISbhqYWnl+2i0vG5jIoLcHvcEQ6JSMpli+dP5wPdx7g9bX7uf6RhVw+Lpd/uqCAqQUZnU7q1NQ38faGEl5bs5f5G0qpa2wmNzWOm6cN5fLTcpkyPIPYwMe9j55ppxfVEdX1TeytrGPPwcPsqaxj78E6fvHWJlwHm8VEG6cNSuXO6aO47LRczshLY87SXZ2Kvz0ZSbH87IaJXDwmh3teXsWMhz/gN5+fzLQRmadUb6RQgkpERCQMrd9bRV56ghIRAkCUGdedOZhfvV3EKyv2cLM3mXoozyzeeVzZTR2sLyKR6c11+ymrbuDmc/T6lshiZkwelsEZeelU1zfymwVbeXPdfkblJHPV+IFMG5HJhPw0kuMCRxNWdQ3NbNpfxcriSt7fXMa7m0qpb2ohKzmOmZPzueqMQUwtyOjyUNfkuACFuSkU5n48tcKMSYNZv/cQRSXV1DU2c7ixheS4aDKT48hLT2DMwJSjFznpbldPGMQZeWl86Q9L+PzvFnP/9RP4zFl9f15SJahERETCTFNzCx9sKeOSMTl+hyJhJCclnk+Oy+W1Nft4v6iMm88Z1iOPEyrBBUpyiYQT5xxPLtxO/oAELizM9jsckS6JDURx1/mFzL5gBK+u2sNzS3fx0Pwifvl20dH7U+NjqKlvoq6x+eh2A1PjuXHqUK46YxCThw3osfnXkuICTBme4dvQ+qGZibx8x/nc/sfl3P38SnaU1/KNywr79NWdlaASEREJM0u3H6CytpHLx+X6HYqEmQtGZbGzopbX1+5j4ZZyzh15fJf/neU1rNt7iAFJsQxMjWdIRqIPkYpIT3p3UylLtlVw77XjNDm6RLyE2GhumDKEG6YMoepwI8t3HGDjvioqaho4dLiR5LgA6YmxFGQlMXFIOoNPcn6nSJaWGMOTX57K9/68mgff2szOilp+cv0ZxAV6pueW35SgEhERCTNvrttPbCCKC0frV3E5lplx/Vn5PPLOFu7403K+cWkhs6YOJcqMVcWVPLZgK2+s248BR6bNGJmdxLUTB5OW0P5w0ZYWx3PLdjFn6S7iAlGcNjCFkTnJBKI6vnqQeluJ9L7mFsdPXtvAsMxEbprWMz0pRfySEh/DxWNyuLiDXuTtnXu6W289zonEBqL42cwJDM9M5H/f2MTuA3X85pbJDEgKPW9lJFOCSkREJIw453hz/T7OH5lJUpxO03K8+Jhobjl3GO9vLuPev67jwbc2t5obI8Dl43I5b2QmtQ3NbNh7iHmr9/GZX3/AE7eeHfJKQBv3VfH9P69m2Y4DFOYks35PLUu2VZCXnsBN04YyoJ2J20XEHy99WMyGfVU8fNNZx0wCLSJ9l5lx1yWFDM1M4lsvrOQzj/yDJ249m4KsvnWFP33yFRERCSMb91exq6KOOy4a5XcoEsaykuO47sw8JgxJY8m2ClLiAgzLTGJkdjIJscFu/3GBaM4dmUVuWjwvLi/mqgff485LRjH7ggLiAtHUNTTzq7c389iCraTEB/jZzAnMnJzPUwt3sG7PIf6yYjcPzy/ihslDGJ2bfFwMB+sa+WjnATbur6L4QB0js5M5Z0SGriYm0oNqG5r4+RubmDgknavOGOh3OCLSyz49cTCD0+L556eW8X9+/QGP3TKFqQX+zJHVE5SgEhERCSNvrt0PwGWnaYJ0ObERWcmMyDo+edR2nb/edQH3vbqOn/5tI0+8v534mCgO1DRQ09DMzMn5fO+q08jwhgrEREcxcUg6eekJ/HHxDp5cuJ1BafE0tTjSE2M43NjMgk1lvLl+Pw1NLSTFBchPT2DFrgMs3V7BuEGpfGr8wB4ZeqAhhdKfOef43sur2V91mIduOrPfzMEjIseaMjyDP3/lfL78h6V8/vHF/HTmBK47M8/vsLqFElQiIiJh5M31+5k0JJ2c1Hi/Q5E+5L3NZUwfk0N+egLLdhwgOsrIH5DANy4bzTkjjp9oHSArJY6vXDyKFbsqWbS1nP+Yu/bofQMSY7hp6lASYqLJG5BAlBm1DU0s2lrB/A0lfOrB9/i/n53IeSMzj/sS7Zyj+EAdW0qr2VVRy66KOoZmJhLVZr1QSafahia2lNawvayGrORYJuank3gSQ2Erahr468o9xEQfPyxKSS4Jd88s2clfVuzhm5eP9u2qYiISHoZnJfHyV87jX55ezjeeW8GKXZXcc9XYiJ88XQkqERGRMLFwSzmrig/ynSvH+h2K9FGFuSkU5qYc/b+95NQRsYEophZkcPbwAVTUNNDiIDrKSE0IHDeBemJsgEvG5jBmYArzVu/l5scXMyI7iWvOGMSApFgam1vYsK+KD4rK2H+o/phtk+ICnDYwhXGDUxmZnXw0geSco6y6gQ+Kynjpw2Le31yGAwJRRlOLY97qfYzPS+WCUVkMzQx9tcLy6nrmLN3FG2v3sbL4IElxAS4szGJaQabm75GIsWJXJf85dx0Xjc7mzukaAi4ikJ4Yy9Ozp/GT1zbwxAfbWL7jAA/OmsSI7I57VoczJahERETCwKHDjXzz+RVkJsWSEBMdNleOkd4Vrs+7mZGZHNepdfPSE/jiucNZsauSlcWV/OrtoqNXFMxIiuW8kZlMG5HJmNwU8gYk8Ku3NrN2zyFW7z7Ish0HiLLg/Fm/fGszB2obqG9qOVrvRaOzGTMwhfwBiZRUHebDHQdYsr2Cy37+Lp8/ZxiXnpbD+Lw0mlscO8prmLd6L39ctJO6xmYmDUnn7stHM3flHl5bs48Pisr47JQhPfZBfldFLe9uKmX5jgNMzE8/Lhl2qj22NNyx//igqIx/eXo5OalxPPC5SURFaWifiATFBqL44bXjOGdEBv/24iqufPA97po+itsvGhmRP8IoQSUiIhIG7n1lLfur6rntEyMi8gOFSGtHel5NLcjgcGMzLc4RbcYXzxt+3JfrCfnpTMhPp6m5ha1lNWwvr6G+sYWGphYKc5JJS4xhUFoCw9oMAxyUlsDVExK4oDCbraXV/P4f23jig23H1B1lcN2kPL4yfRSjcoKJqKzkOHaU1/DSh7v53fvbuGRsDp8ozA65H48t2Ep5dT0HahtJjI2mICuJmOiodpNALS2OeWv28qu3iti4v+po+d/W7GNqQQYXjc4mPqb7h180tzj2HTzMtvIaSqoOM+vsoQxM0zDhSOec4y8rdvPtF1cxIiuZJ7889ehccSIirX3y9IFMGprOfX9dx8/f3MSfP9rNNy4r5JoJg4mOoKS2ElQiIiI+qqxt4MG3NvPyR7v5+qWF5GruKeljWidkOur5EYiOYnRuCqNbDUHsjLSEGM4cOoAxA1PYXVnHnsrDBKKMjKRY7rh4JIPTj7+q4LDMJO6cPpK5K/bw1oYS3ttcxrIdFYzOTaGxuYX9hw6zZFsF28trj40xyhiRnURDUzMXjs4mJzWeQJSxo7yWD4rKePmjYtbsPsTo3GT+49pxXDg6m+eX7uL9ojIWbCplxa5Krps0mDEDU9vdn+YWR2VtAzGBKFLjYzrcd+cc6/ce4tXVe6msbQTgtTXw6/lbuH5yPv96eSE5KXpPiUTby2q4969reWdjKVOHZ/DbL0whLbHj40FE+reclHgeuuksrp9cwv2vbeDrc1bw0NtFfOHcYcw4M++E55Rw0GcSVGZ2JfAgEA087pz7ic8hiYj0eyd6bzazOOApYDJQDnzOObfdu+8eYDbQDHzNOfd6R3WaWQEwB8gAPgRucc41dPQYfnHOsWFfFa+t2ceT/9jOocON3Dh1CHddMooXlhX7GZpIxEqMDVCYk0JhzscJrlDJqSPiAtHcMGUIk4cPYNWug/xjSzmvrtqLWTDpNWVYBqcNSiUnJY70xFgO1jWycX8Vm/ZVce9f14Wsc2R2Ej//7ERmTMo7+ov1sMwkhmUmsbOilpc/LObJhTvIS09g38E6xg5Kpaa+ifKaBlbsrOQfW8qoOtyEAwzIH5DA6NwUvnppIWfkpR2ts7nFsXl/FR9sKWPT/mpyU+P47JQhFGQlcc2EQfxmwRaeX1rMa2v2ct+M8Vw7YVDYXvFN54mPNTW38MGWcp5fuos31u0jLhDNv18zji+cOyzkxP4iIqFMH5PDRYXZ/L/Ve3nknS38+ytr+e8I47xlAAAgAElEQVR5G5g+NpvpY3K4cHR22P4g2icSVGYWDTwMXA4UA0vNbK5zLvSnBxER6XGdfG+eDRxwzo0ys1nA/cDnzGwcMAs4HRgM/N3MRnvbtFfn/cADzrk5ZvaoV/cj7T1Gz+79sWrqm9hWVsP6vYdYvK2ChVvK2V1ZhwGFucl84dxhDEpLUHJKel1vz3kVjnNsjchKZkRWMi3O4VxwWGCoZE5uanywd9eE4MTrW0trONzUTHOLIyU+wL9ePpr8AaEnagcYmpHIXdNHsWhrOWv3HOKh+UW0uGPvL8hKIjM5jqTYaGoamtm8v4q3N5Tw1oYSBiTGMDQzidhoY2dFLfsP1RMfE8VV4wdy7siso8mrIRmJ/Oi6M7j1vAK+9cJKvvbsR/xx0Q5mTs7nyvEDw+oX9P54nqipb+JAbQOH6pqorG1g14FadpTXsqr4IB/uPEBtQzMDEmO45Zzh3H7RCF3RVUS6JCrKuHbiYK6ZMIjVuw/y3NJd/H39fuat3gfAwNR4zshPY0R2EsMykshKjiUlPoaU+IB3iyE1PkCgl5PjfSJBBUwFipxzWwHMbA4wA+jWBNX8DSX8ZsGWkPf9+aPI+VLh3InX6U1hFg4ujBoofCIJCqOmAcKsfcKscWZOGcIt5wzzO4zOvDfPAO71ll8EHrLgN8MZwBznXD2wzcyKvPoIVaeZrQcuAW7y1nnSq/eR9h7D9cCL/bmlO5mzdBctLY7GZkdNQxOH6ho54A29ARiQGHN0bp6xA1NICaMviyJ9zckkxKLMgt2WOiEzOe64SeMXbCo74XaB6CguKMzmgsJsahuaOFjXSFwgmoSYaBJij5+b6rLTcqmtb2JzSTWbS6qprm+kqsWRlRzHJWNzGTswpd2eNaNyknnx9nN5cuEOnlq4nW+/uIpvv7iK9MQYclPi+cZlhXzqjEGd2+Ge0+/OE//+yhpe/nD3MWXRUcbo3BRmTs7nvJGZTB+bE/GXiheR8GBmR+d6/NF141m7J/hj6ariStbsPsi7G0tpaG4Jue0Tt07hkrG5vRpvX0lQ5QG7Wv1fDExrvYKZ3Qbc5v1bbWYbu/hYWcCJP4H0Hdrfvqs/7Sv0s/2dC1lf6Pr+dldm64Tvza3Xcc41mdlBINMrX9Rm2zxvOVSdmUClc64pxPrtPcYx7dON54kO7QBWBBf71TF5EtQux1ObhNav22VeO+U3n6Bddnh/3zi1h9d5omvniXafm63A306ysm4Ujq+lcIwJFNfJ6FRMN/dCIG2EY1ud8L3bD5fe3+WYunyO6CsJqlC/dx3zi4dz7jHgsVN+ILNlzrkpp1pPpND+9l39aV9B++tXGCHK2v4a3d467ZWH6irQ0fqdjaPbzhOdFSbPUdhRuxxPbRKa2iW0CGuXfnWeCNfnJhzjCseYQHGdjHCMCRTXyfAjpr4y214xMKTV//nAHp9iERGRoM68Nx9dx8wCQBpQ0cG27ZWXAeleHW0fq73HEBERf+k8ISIiR/WVBNVSoNDMCswsluCEiXN9jklEpL/rzHvzXOCL3vJM4G1vzo+5wCwzi/OuulQILGmvTm+b+V4deHW+coLHEBERf+k8ISIiR/WJIX7eWPG7gNcJXk72Cefc2h56uF4b/hEmtL99V3/aV9D+9rr23pvN7D5gmXNuLvA74GlvctsKgl8k8NZ7nuBEuU3Anc65ZoAO3u+/A8wxsx8BH3l1095jhAHfn6MwpXY5ntokNLVLaBHTLv3wPBGuz004xhWOMYHiOhnhGBMorpPR6zGZfhwQERERERERERE/9ZUhfiIiIiIiIiIiEqGUoBIREREREREREV8pQdUOM7vSzDaaWZGZfTfE/XFm9px3/2IzG977UXafTuzv3Wa2zsxWmdlbZjbMjzi7w4n2tdV6M83MmVlYXe7zZHVmf83ss97zu9bMnuntGLtTJ47loWY238w+8o7nq/yIszuY2RNmVmJma9q538zsl15brDKzs3o7Rgmts+9DfYWZbTez1Wa2wsyWeWUZZvammW32/g7wyts9bs3si976m83si+09XrgK9ZrtznYws8leOxd521rv7mHXtNMu95rZbu+YWdH6vdrM7vH2caOZXdGqPOTryoKTZS/22us5C06cHdbMbIh3rlrvnZu/7pX3++PFb+F6vIbjMdNBTL62l5nFm9kSM1vpxfWfHdVlHXzvO9l4uxjXH8xsW6v2muSV99rr3syiLfjZ+dVwaKsO4gqHturRzzxdiaudmHx/3wrJOadbmxvBCRW3ACOAWGAlMK7NOl8BHvWWZwHP+R13D+/vdCDRW74jUve3M/vqrZcCLAAWAVP8jruHn9tCghOFDvD+z/E77h7e38eAO7zlccB2v+M+hf29EDgLWNPO/VcBrwEGnAMs9jtm3Tr/PtSXbsB2IKtN2U+B73rL3wXu95ZDHrdABrDV+zvAWx7g976dZDsc95rtznYgeAWzc71tXgM+5fc+n0K73At8K8S647zXTBxQ4L2Wojt6XQHPA7O85UePnAPC+QYMAs7yllOATd6+9/vjxe9buB6v4XjMdBCTr+3lxZ/sLccAi702CFkX7Xzv60q8XYzrD8DMEOv32useuBt4Bni1o3bvrbbqIK5waKvt9OBnnq7E1U5M9xKG51n1oAptKlDknNvqnGsA5gAz2qwzA3jSW34RuLSzWdUwdML9dc7Nd87Vev8uAvJ7Ocbu0pnnFuC/CL6RHO7N4HpAZ/b3n4GHnXMHAJxzJb0cY3fqzP46INVbTgP29GJ83co5t4Dg1YbaMwN4ygUtAtLNbFDvRCcd6Oz7UF/X+jz6JHBdq/JQx+0VwJvOuQrv/epN4MreDvpUtPOa7ZZ28O5Ldc4tdMFPiE+1qiusdeK9rLUZwBznXL1zbhtQRPA1FfJ15X02u4TgZzU4to3DlnNur3PuQ2+5ClgP5KHjxXfheryG4zHTQUzt6ZX28va52vs3xru5Dupq73vfScV7CnG1p1de92aWD1wNPO7931G790pbhYrrBPx+j4yk925fz7NKUIWWB+xq9X8xx7+ZHl3HOdcEHAQyeyW67teZ/W1tNsFsbSQ64b6a2ZnAEOfcq70ZWA/pzHM7GhhtZh+Y2SIzi6gvfG10Zn/vBT5vZsXAPOCrvROaL072tS29oz8+Lw54w8yWm9ltXlmuc24vBL/EADleeXvt01fbrbvaIc9bblseye7yhjw8cWQ4BCffLplApfdZrXV5xPCGyJxJsDeFjpfwFTbHazgeM21iAp/byxsatgIoIfjlf0sHdbX3va/bz1dt43LOHWmvH3vt9YCZxbWNq5OP39Xn8BfAt4EW7/+O2r3X2ipEXEf42VbQs595uhpXqJggjN63jlCCKrRQPaHaZq87s06k6PS+mNnngSnAz3o0op7T4b6aWRTwAPDNXouoZ3XmuQ0QHOZ3MXAj8LiZpfdwXD2lM/t7I/AH51w+wW61T3vPe1/Ul96n+pL++Lyc75w7C/gUcKeZXdjBuu21T39rt5Nth77WPo8AI4FJwF7g/3rl/apdzCwZeAn4hnPuUEerhijrs+0ShsLmeA3HYyZETL63l3Ou2Tk3ieCokKnAaR3U5VtcZjYeuAcYC5xNcMjXd3orLjO7Bihxzi1vXdxBPb3SVu3EBT62VSs9+Zmnq3GFisn312EoffVL2akqBoa0+j+f44cBHV3HzAIEhwp1trtvuOnM/mJmlwHfBz7tnKvvpdi624n2NQUYD7xjZtsJjgWea5E7UXpnj+VXnHONXjfOjQQTVpGoM/s7m+A4aZxzC4F4IKtXout9nXptS6/rd8+Lc26P97cE+DPBLwP7jww59f4eGV7cXvv01XbrrnYo5tjh9xHdPs65/d4XtRbgtwSPGTj5dikjOGQi0KY87JlZDMEv9X9yzr3sFet4CUPhcryG4zETKqZwaS8vlkrgHYKf+durq73vfT12vmoV15XeUEnnff/6PV1vr648h+cDn/a+F80hOJTrF/jfVsfFZWZ/9LmtgB7/zNOluELFFE6vw7bB6nb8xGABghORFfDxBGCnt1nnTo6dAO55v+Pu4f09k2DX10K/4+3pfW2z/jtE9iTpnXlurwSe9JazCHbdzPQ79h7c39eAW73l07w3UPM79lPY5+G0P0n61Rw78eISv+PV7eTfhyL9BiQBKa2W/+G97/yMYycM/am3HPK4JfhL6DaCk4UO8JYz/N6/LrTHMa/Z7mwHYKm37pGJU6/ye39PoV0GtVr+V4LzYQCczrGTt24lOHFru68r4AWOnbz1K37vbyfawwjOLfKLNuU6XsLgFo7HazgeMx3E5Gt7AdlAurecALwHXNNeXbTzva8r8XYxrkGt2vMXwE966zlsE9/FfDwZua9t1UFcvrYVvfCZ52Tj6iAm39+3Qsbb1Q37+o3g0J9NBJMy3/fK7iPYewiCvS5eIDhp2BJghN8x9/D+/h3YD6zwbnP9jrmn9rXNuu8QwQmqTj63BvwcWAesPvLmEqm3TuzvOOAD7011BfBJv2M+hX19lmCX3EaCv2rMBm4Hbm/13D7stcXqSD+W+9It1HHaV28Er/ay0rutbfW6zATeAjZ7f4988Gr3uAW+7J13i4Av+b1vXWiLUK/ZbmsHgkPw13jbPESEJN/baZenvf1eBczl2A/S3/f2cSOtrl7U3uvKOwaXeO31AhDn9z53ok0uIDhEYhUff/a6SseL/7dwPV7D8ZjpICZf2wuYQPAK1qu8/flhR3XRwfe+k423i3G97bXXGuCPfHylv1593XNsIsjXtuogLl/bil74zHOycXUQk+/vW6Fu5lUoIiIiIiIiIiLiC81BJSIiIiIiIiIivlKCSkREREREREREfKUElYiIiIiIiIiI+EoJKhERERERERER8ZUSVCIifYSZPWFmJWa2phPrDjWz+Wb2kZmtMrOreiNGERERERGRUJSgEmnFzNLN7CunWMetZvZQJ9ZxZnZpq7L/45XNPJXHl37tD8CVnVz3B8DzzrkzgVnAr3sqKJG+ROcJERHpiM4TIl2nBJXIsdKB404oZhbdA4+1Grix1f+zgJU98DjSTzjnFgAVrcvMbKSZ/c3MlpvZe2Y29sjqQKq3nAbs6cVQRSKZzhMiItIRnSdEukgJKpFj/QQYaWYrzGypNwTqGYJv/pjZX7wv+mvN7LYjG5nZl8xsk5m9C5zfqjzbzF7y6lpqZue3eqz3gKlmFmNmycAoYEWrbS/1hl+t9oZuxXnl283sP83sQ+++sV55krfeUm+7GV75e2Y2qVW9H5jZhB5oOwlPjwFfdc5NBr7Fxz2l7gU+b2bFwDzgq/6EJxJxdJ4QEZGO6Dwh0kVKUIkc67vAFufcJODfgKnA951z47z7v+x90Z8CfM3MMs1sEPCfBE8klwPjWtX3IPCAc+5s4Hrg8Vb3OeDvwBXADGDukTvMLJ7gcK3POefOAALAHa22LXPOnQU8QjDpAPB94G3vsaYDPzOzJO8xb/XqHQ3EOedWda15JJJ4H1TOA14wsxXAb4BB3t03An9wzuUDVwFPm5nOCSInpvOEiIh0ROcJkS7SlxGRji1xzm1r9f/XzGwlsAgYAhQC04B3nHOlzrkG4LlW618GPOQlB+YCqWaW0ur+OQS74s4Cnm1VPgbY5pzb5P3/JHBhq/tf9v4uB4Z7y58Evus91jtAPDAUeAG4xsxigC8TPFFJ/xAFVDrnJrW6nebdNxt4HsA5t5Dg8ZLlU5wikUznCRER6YjOEyKdFPA7AJEwV3NkwcwuJniCONc5V2tm7xB804bgrxehRHnr17UuNLPgRs4tMbPxQJ1zbtORcsDoWL33t5mPX8cGXO+c29h2ZTN7k+CvKp8l+GuN9APOuUNmts3MbnDOvWDBA2yCc24lsBO4FPiDmZ1G8Fgu9TNekQil84SIiHRE5wmRTlIPKpFjVQEp7dyXBhzwTiZjgXO88sXAxV733BjghlbbvAHcdeSf1mO3W7kH+F6bsg3AcDMb5f1/C/DuCWJ/Hfiql4TAzM5sdd/jwC+Bpc65ilAbS+Qzs2eBhcAYMys2s9nAzcBs75e6tQQ/WAB8E/hnr/xZ4FbnXHsfjETkYzpPiIhIR3SeEOki9aASacU5V+5N+rcGqAP2t7r7b8DtZrYK2EiwWy7Oub1mdi/BxMBe4EPgyFU6vgY87G0TABYAt7d5zNdCxHHYzL5EcO6gALAUePQE4f8X8AtglXdS2Q5c49W33MwOAb/vTDtIZHLO3djOXVeGWHcdrSbgFJHO0XlCREQ6ovOESNeZfjAX6fvMbDDBceRjnXMtPocjIiJhRucJERHpiM4T0hs0xE+kjzOzLxDsNvx9nUxERKQtnSdERKQjOk9Ib1EPKhERERERERER8ZV6UImIiIiIiIiIiK+UoBIREREREREREV8pQSUiIiIiIiIiIr5SgkpERERERERERHylBJWIiIiIiIiIiPhKCSoREREREREREfGVElQiIiIiIiIiIuIrJahERERERERERMRXSlCJiIiIiIiIiIivlKASERERERERERFfKUElIiIiIiIiIiK+UoJKRERERERERER8pQSViIiIiIiIiIj4SgkqERERERERERHxlRJUIiIiIiIiIiLiKyWoRERERERERETEV0pQiYiIhGBmT5hZiZmt6Ya6ppvZila3w2Z2XXfEKSIiIiLSF5hzzu8YREREwo6ZXQhUA08558Z3Y70ZQBGQ75yr7a56RUREREQimXpQiYiIhOCcWwBUtC4zs5Fm9jczW25m75nZ2C5UPRN4TckpEREREZGPKUElIiLSeY8BX3XOTQa+Bfy6C3XMAp7t1qhERERERCJcwO8AREREIoGZJQPnAS+Y2ZHiOO++zwD3hdhst3PuilZ1DALOAF7v2WhFRERERCKLElQiIiKdEwVUOucmtb3DOfcy8HIn6vgs8GfnXGN3ByciIiIiEsk0xE9ERKQTnHOHgG1mdgOABU08yWpuRMP7RERERESOowSViIhICGb2LLAQGGNmxWY2G7gZmG1mK4G1wIyTqG84MAR4t/ujFRGRUMzsCTMrMbM17dxvZvZLMysys1VmdlZvxygiIkGdSlCZ2ZVmttF74/5uiPvjzOw57/7F3ofwI/fd45VvNLMrTlSnmRV4dWz26oz1ym83s9VmtsLM3jezcV75cDOr88pXmNmjXW8OERGRIOfcjc65Qc65GOdcvnPud865bc65K51zE51z45xzoeadaq++7c65POdcS0/GLSIix/gDcGUH938KKPRutwGP9EJMIiISwgkTVGYWDTxM8M17HHDjkeRQK7OBA865UcADwP3etuMIXq3odIInhl+bWfQJ6rwfeMA5Vwgc8OoGeMY5d4Y398dPgZ+3evwtzrlJ3u32k2sCERERERHpi5xzC4CKDlaZATzlghYB6d4FLUREpJd1ZpL0qUCRc24rgJnNIfhGvq7VOjOAe73lF4GHLHiJoxnAHOdcPcF5O4q8+ghVp5mtBy4BbvLWedKr9xFv7o8jkgB3Evt5jKysLDd8+PCubi4i0qctX768zDmX7XccftJ5QkSkfX3sPJEH7Gr1f7FXtrftimZ2G8FeViQlJU0eO3ZsrwQoIhJJTuUc0ZkEVag37WntreOcazKzg0CmV76ozbZ53nKoOjMJXiGpKcT6mNmdwN1ALMFE1hEFZvYRcAj4gXPuvY52aPjw4SxbtqyjVURE+i0z2+F3DH7TeUJEpH197DxhIcpC/hDunHsMeAxgypQpTucJEZHjnco5ojNzUHXmTbu9dbqrPLjg3MPOuZHAd4AfeMV7gaHOuTMJJq+eMbPUtpWY2W1mtszMlpWWloZ4GBERERER6WeKCV7A4oh8YI9PsYiI9GudSVB15k376DpmFgDSCI71bm/b9srLCI77DrQpb2sOcB2Ac67eOVfuLS8HtgCj227gnHvMOTfFOTclO7uv9EgWEREREZFTMBf4gnc1v3OAg86544b3iYhIz+tMgmopUOhdXS+W4KTnc9usMxf4orc8E3jbOee88lneVf4KCF4dY0l7dXrbzPfqwKvzFQAzK2z1eFcDm73ybG/SdcxshPcYWzvbACIiIiIi0jeZ2bPAQmCMmRWb2Wzv6uBHLqw0j+B3hyLgt8BXfApVRKTfO+EcVN6cUncBrwPRwBPOubVmdh+wzDk3F/gd8LQ3CXoFwYQT3nrPE5xQvQm40znXDBCqTu8hvwPMMbMfAR95dQPcZWaXAY0Er+53JCF2IXCfmTUBzcDtzrmOrtQhIiIiIiL9gHPuxhPc74A7eykcERHpQGcmScc5N4/grwuty37YavkwcEM72/4Y+HFn6vTKt/Lxlf5al3+9nfpfAl7qeA9ERERERERERCRcdWaIn4iIiIiIiIiISI9RgkpERERERERERHylBJWIiIiIiIiIiPhKCSoREREREREREfGVElQiIiIiIiIiIuIrJahERERERERERMRXSlCJiIiIiIiIiIivAn4HIH3DM4t3+h1Ch26aNtTvEESkH2nvPVHvRSIiIiIioakHlYiIiIiIiIiI+EoJKhERERERERER8ZUSVCIiIiIiIiIi4islqERERERERERExFdKUImIiIiIiIiIiK+UoBIREREREREREV8pQSUiIiIiIiIiIr5SgkpERERERERERHylBJWIiIiIiIiIiPhKCSoREREREREREfGVElQiIiIiIiIiIuIrJahERCTimVm0mX1kZq+GuC/OzJ4zsyIzW2xmw3s/QhERERER6YgSVCIi0hd8HVjfzn2zgQPOuVHAA8D9vRaViIiIiIh0ihJUIiIS0cwsH7gaeLydVWYAT3rLLwKXmpn1RmwiIiIiItI5SlCJiEik+wXwbaClnfvzgF0Azrkm4CCQ2XYlM7vNzJaZ2bLS0tKeilVEREREREJQgkpERCKWmV0DlDjnlne0Wogyd1yBc48556Y456ZkZ2d3W4wiIiIiInJiSlCJiEgkOx/4tJltB+YAl5jZH9usUwwMATCzAJAGVPRmkCIiIiIi0jElqEREJGI55+5xzuU754YDs4C3nXOfb7PaXOCL3vJMb53jelCJiIiIiIh/An4HICIi0t3M7D5gmXNuLvA74GkzKyLYc2qWr8GJiIiIiMhxlKASEZE+wTn3DvCOt/zDVuWHgRv8iUpERERERDpDQ/xERERERET+P3t3Ht5Xed95//3V5n2V5X3HwmCzGJANCVkIIcEkU8hMkwbaZMgMGZ60YTK50ukTMpmHzNDmamk7pTMNaUJLWppJ4lAyk7ipEwoBQgjxIna8YXk3xpZsybu1388fOhAhJEtepKPl/bouXfn97nOf+3x+isHoq/t8jyQpVxaoJEmSJEmSlCsLVJIkSZIkScqVBSpJkiRJkiTlygKVJEmSJEmScmWBSpIkSZIkSbnqUYEqIpZHxOaIqIqIOzs5Piwivp8dXxMRc9sd+1I2vjkiru9uzYiYl62xJVuzJBv/TES8HBEvRMTTEbGou2tIkiRJkiSp/+u2QBURhcB9wA3AIuCW9sWhzG1AXUppAXAvcE927iLgZmAxsBz4ekQUdrPmPcC9KaVyoC5bG+C7KaWLU0pLgD8F/uJU1zjt74QkSZIkSZJy0ZMdVMuAqpTStpRSI7ACuKnDnJuAB7PXDwPvj4jIxleklBpSStuBqmy9TtfMzrk2W4NszY8ApJSOtLveKCC1u3Zn15AkSZIkSdIA0JMC1Qxgd7v3e7KxTueklJqBw0DpKc7tarwUOJSt8bZrRcRnI2IrbTuoPnca+YiI2yOiMiIqa2pquvnIkiRJkiRJ6is9KVBFJ2Oph3PO1Xjbi5TuSymdB3wR+K+nkY+U0v0ppYqUUkVZWVknp0iSJEmSJCkPPSlQ7QFmtXs/E9jb1ZyIKALGAbWnOLer8QPA+GyNrq4FbbcEfuQ08kmSJEmSJKmf6kmBah1Qnj1dr4S2huQrO8xZCdyavf4o8HhKKWXjN2dP+ZsHlANru1ozO+eJbA2yNX8EEBHl7a73YWBLu2t3dg1JkiRJkiQNAEXdTUgpNUfEHcAjQCHwrZTS+oi4G6hMKa0EHgC+HRFVtO2cujk7d31EPARsAJqBz6aUWgA6WzO75BeBFRHxR8Dz2doAd0TEdUATbU/3u7W7a0iSJEmSJKn/67ZABZBSWgWs6jB2V7vX9cDHujj3q8BXe7JmNr6NTp7Cl1L6T6fI1+k1JEmSJEmS1P/15BY/SZIkSZIkqddYoJIkSZIkSVKuLFBJkiRJkiQpVxaoJEmSJEmSlCsLVJIkSZIkScqVBSpJkiRJkiTlygKVJEmSpEErIpZHxOaIqIqIOzs5PjsinoiI5yPipYj4UB45JWmos0AlSZIkaVCKiELgPuAGYBFwS0Qs6jDtvwIPpZQuA24Gvt63KSVJYIFKkiRJ0uC1DKhKKW1LKTUCK4CbOsxJwNjs9Thgbx/mkyRlLFBJkgasiBgeEWsj4sWIWB8R/72TOZ+KiJqIeCH7+nQeWSVJuZgB7G73fk821t5/Az4REXuAVcB/7JtokqT2LFBJkgayBuDalNKlwBJgeURc1cm876eUlmRff9u3ESVJOYpOxlKH97cAf59Smgl8CPh2RLzt56SIuD0iKiOisqampheiStLQZoFKkjRgpTbHsrfF2VfHHzwkSUPXHmBWu/czefstfLcBDwGklH4FDAcmdVwopXR/SqkipVRRVlbWS3ElaeiyQCVJGtAiojAiXgCqgUdTSms6mfab2ZOZHo6IWZ0c9zfjkjQ4rQPKI2JeRJTQ1gR9ZYc5u4D3A0TEhbQVqPyLQJL6mAUqSdKAllJqSSktoe234ssi4qIOU/4JmJtSugR4DHiwi3X8zbgkDTIppWbgDuARYCNtT+tbHxF3R8SN2bTfB/5DRLwIfA/4VErJ3biS1MeK8g4gSdK5kFI6FBFPAsuBV9qNH2w37W+Ae/o4ml96bEUAACAASURBVCQpRymlVbQ1P28/dle71xuAq/s6lyTprdxBJUkasCKiLCLGZ69HANcBmzrMmdbu7Y20/QZdkiRJUj/iDipJ0kA2DXgwIgpp+6XLQymlH0fE3UBlSmkl8LnsNo5moBb4VG5pJUmSJHXKApUkacBKKb0EXNbJePtbN74EfKkvc0mSJEk6Pd7iJ0mSJEmSpFxZoJIkSZIkSVKuLFBJkiRJkiQpVxaoJEmSJEmSlCsLVJIkSZIkScqVBSpJkiRJkiTlygKVJEmSJEmScmWBSpIkSZIkSbmyQCVJkiRJkqRcFeUdQDpTDc0tbN53lKrqYxw62cSRk00UFgSjSoqYMKqY+ZNGM79sFGOGF+cdVZIkSZIknYIFKg04h0828diG/by45xDNrYkRxYWUji6hbMwwWloTxxuaeWnPCdbtqCOAhVPHMGviCN61YBIRkXd8SZIkSZLUgQUqDRgtrYknX63mqVdraE1QMWcCl8wcz5zSkRR0KDy1tCZeP3ySDa8fYd32Wj75wFoumz2eu2+8iItnjsvpE0iSJEmSpM5YoNKAcKKxmRXrdlNVfYyLZ4zj+sVTmTiqpMv5hQXBzAkjmTlhJNcunExJUQF//i+vcuN9T/OJK+fw5Q9fyPDiwj78BJIkSZIkqSsWqNTvHTzWwN8/s4NDJ5v4zctncMWciad1flFhATcvm82HLpnGvY++yt/9cgfrdtTy9d+5nPllo3sptSRJkiRJ6imf4qd+7Uh9E9/65XZONrXw6XfNO+3iVHtjhxfzld9YzN/9u6XsP1LPb/zV0zz1as05TCtJkiRJks5EjwpUEbE8IjZHRFVE3NnJ8WER8f3s+JqImNvu2Jey8c0RcX13a0bEvGyNLdmaJdn4FyJiQ0S8FBE/i4g57c5piYgXsq+VZ/atUH9T39TCg8/s4HhDC59651zmlI46J+u+b+Fk/vlz72Z26Shue3Ad//zS6+dkXUmSJEmSdGa6LVBFRCFwH3ADsAi4JSIWdZh2G1CXUloA3Avck527CLgZWAwsB74eEYXdrHkPcG9KqRyoy9YGeB6oSCldAjwM/Gm7659MKS3Jvm48re+A+qXWlPje2l3sP1LP71w5m5kTRp7T9aePH8GK269iyazx3PG953iocvc5XV+SJEmSJPVcT3pQLQOqUkrbACJiBXATsKHdnJuA/5a9fhj4WkRENr4ipdQAbI+Iqmw9OlszIjYC1wK/nc15MFv3r1NKT7S73mrgE6fxOTXAPFN1gC3Vx7hpyXTKp4w56/W+u2ZXp+Mfvng6h0408cWHX+Kl3YdYND2fJ/z99pWzc7muJEmSJEn9QU9u8ZsBtN9esicb63ROSqkZOAyUnuLcrsZLgUPZGl1dC9p2Vf2k3fvhEVEZEasj4iOdfYiIuD2bU1lTY9+h/mzvoZM8smE/i6aNZdncM+851RMlRQX8zpVzmDFhBCvW7Wb7geO9ej1JkiRJkvR2PSlQRSdjqYdzztX4ry8U8QmgAvizdsOzU0oVtO28+suIOO9ti6R0f0qpIqVUUVZW1sll1B80tbTyUOVuRpYU8q8vm0HbRrzeVVJUwK3vmMv4kSV8e/UOao839vo1JUmSJEnSr/WkQLUHmNXu/Uxgb1dzIqIIGAfUnuLcrsYPAOOzNd52rYi4DvgycGN22yAAKaW92f9uA54ELuvB51I/9EzVAaqPNvCbl89k1LCe3IF6bowaVsSn3jmXIPjOmp00Nrf22bUlSZIkSRrqelKgWgeUZ0/XK6Gt6XnHJ+WtBG7NXn8UeDyllLLxm7On/M0DyoG1Xa2ZnfNEtgbZmj8CiIjLgG/SVpyqfuPCETEhIoZlrycBV/PW/lgaII6cbOKJzTVcOG0s55+DvlOna+KoEn6rYib7Dtez8sXXaPvjKEmSJEmSelu3W1RSSs0RcQfwCFAIfCultD4i7gYqU0orgQeAb2dN0GtpKziRzXuItoJRM/DZlFILQGdrZpf8IrAiIv6Itif3PZCN/xkwGvjH7LavXdkT+y4EvhkRrbQV3P4kpWSBagD6lw37aEmJD100NbcMC6eO5X0XTObxTdXMnzSay+dMyC2LJEmSJElDRY/uoUoprQJWdRi7q93reuBjXZz7VeCrPVkzG9/Gr5/01378ui7Wfwa4+NSfQP3dnroTPLfrEO8pn0Tp6GG5Zrn2gslsqznOP720l/lloxg/siTXPJIkSZIkDXY9ucVP6nWPbdzPyJJCrlk4Oe8oFETw0StmkhL84Lk9tHqrnyRJkiRJvcoClXL3Wt1JXt1/jHctmMTw4sK84wBt/ag+dPE0ttYcZ8322rzjSOpCRAyPiLUR8WJErI+I/97JnGER8f2IqIqINRExt++TSpIkSToVC1TK3RObqxleXMBV80vzjvIWS+dOoHzyaP5l/T6O1DflHUdS5xqAa1NKlwJLgOURcVWHObcBdSmlBcC9wD19nFGSJElSNyxQKVf7jtSz4fUjvPO8/rN76g0RwW9cOp3m1sRPXn497ziSOpHaHMveFmdfHe/LvQl4MHv9MPD+yJ62IUmSJKl/sEClXP18czUlRQW8s5/tnnrDpNHDeE95GS/uOczWmmPdnyCpz0VEYUS8AFQDj6aU1nSYMgPYDW1PpgUOA2/7l05E3B4RlRFRWVNT09uxJUmSJLVjgUq5OVLfxMuvHWbpnAmMHNajB0rm4pqFZUwYWczKF/fS0mrDdKm/SSm1pJSWADOBZRFxUYcpne2Wets/zCml+1NKFSmlirKyst6IKkmSJKkLFqiUm8odtbQmuLKf7p56Q3FhAR++eDo1Rxuo3GnDdKm/SikdAp4Elnc4tAeYBRARRcA4wH+YJUmSpH7EApVy0dKaWLu9lvLJo5k0eljecbp14bQxzCkdyc82VtPQ3JJ3HEmZiCiLiPHZ6xHAdcCmDtNWArdmrz8KPJ5ScjukJEmS1I9YoFIuNr5+hCP1zf3uyX1diQhuuGgaxxqaeXrLgbzjSPq1acATEfESsI62HlQ/joi7I+LGbM4DQGlEVAFfAO7MKaskSZKkLvTfxj8a1NZsP8j4EcUsnDom7yg9NnviSBZPH8svthxg2byJjBlenHckachLKb0EXNbJ+F3tXtcDH+vLXJIkSZJOjzuo1OcOHGtga81xls2bSMEAe9L79Yum0tzaylOv+oQvSZIkSZLOFQtU6nPP7zpEAJfPnpB3lNM2acwwlswaz5rttRytb8o7jiRJkiRJg4IFKvWplBIv7K7jvMmjGTtiYN4i976Fk2lNyV1UkiRJkiSdIxao1Kd2HjxB3YkmLps1Pu8oZ6x0dNsuqrU73EUlSZIkSdK5YIFKfer53XUUFwaLpo/NO8pZuWbhZJpbEr/wiX6SJEmSJJ01C1TqM00trbz82mEWTx/HsKLCvOOclUmjh3HprPGs3V7LycaWvONIkiRJkjSgWaBSn9m07yj1Ta0D+va+9t5dPonGllbWbD+YdxRJkiRJkgY0C1TqMy/vOcToYUWcN3l03lHOiWnjRnD+lNH8cutBmlpa844jSZIkSdKAZYFKfaKxuZXN+4+yaPpYCiLyjnPOvLu8jOMNzTy/61DeUSRJkiRJGrAsUKlPbKk+SlNL4qLp4/KOck7NnzSKGeNH8IstNbSmlHccSZIkSZIGJAtU6hPr9x5hZEkh8yaNyjvKORURvLt8EgePN/LqvqN5x5EkSZIkaUCyQKVe19zSysbXj3DhtLEUFgye2/vesHj6OMYOL+JX22yWLkmSJEnSmbBApV5XVXOMhuZWLpo+Nu8ovaKwIFg2byJbqo9Rc7Qh7ziSJEmSJA04FqjU69a/doRhRQWcVzY4nt7XmaVzJ1JYEKx2F5UkSZIkSafNApV6VWtKbNx3hAumjqGocPD+cRszvJiLZ4zjuV11NDS15B1HkiRJmYhYHhGbI6IqIu7sYs5vRcSGiFgfEd/t64ySJAtU6mW7a09worGFC6cNztv72nvH/FIamlt5bvehvKNIkiQJiIhC4D7gBmARcEtELOowpxz4EnB1Smkx8Pk+DypJskCl3rV531EKAsonj8k7Sq+bNXEkMyeMYPXWg6SU8o4jSZIkWAZUpZS2pZQagRXATR3m/AfgvpRSHUBKqbqPM0qSsEClXrZp31HmlI5iRElh3lH6xDvml1JzrIGqmmN5R5EkSRLMAHa3e78nG2vvfOD8iPhlRKyOiOWdLRQRt0dEZURU1tTU9FJcSRq6LFCp1xw60ci+I/VcMHXw7556w8UzxjGqpJDVW22WLkmS1A9EJ2Mdt7oXAeXANcAtwN9GxPi3nZTS/SmlipRSRVlZ2TkPKklDnQUq9ZrN+48CsHDK0ClQFRUWsHTeRDbtO0rt8ca840iSJA11e4BZ7d7PBPZ2MudHKaWmlNJ2YDNtBStJUh+yQKVes+n1o0wcVULZmGF5R+lTV84rJQLWbHMXlSRJUs7WAeURMS8iSoCbgZUd5vwQeB9AREyi7Za/bX2aUpJkgUq9o7G5la01x1g4dQwRne2sHrzGjSjmwmljqdxZR1NLa95xJEmShqyUUjNwB/AIsBF4KKW0PiLujogbs2mPAAcjYgPwBPAHKSV/0yhJfawo7wAanHYcPE5zaxpSt/e1d+W8UtbvPcIrrx3mstkT8o4jSZI0ZKWUVgGrOozd1e51Ar6QfUmSctKjHVQRsTwiNkdEVUTc2cnxYRHx/ez4moiY2+7Yl7LxzRFxfXdrZttv10TElmzNkmz8CxGxISJeioifRcScdufcms3fEhG3ntm3QufSlv1HKSoI5k0alXeUXMwvG0XpqBLW7qjNO4okSZIkSf1etwWqiCgE7gNuABYBt0TEog7TbgPqUkoLgHuBe7JzF9F2n/diYDnw9Ygo7GbNe4B7U0rlQF22NsDzQEVK6RLgYeBPs2tMBL4CXAksA74SEW5ZydmW6mPMnTSK4sKheRdpQQTL5k1k58ET7DtSn3ccadCKiFkR8UREbIyI9RHxnzqZc01EHI6IF7KvuzpbS5IkSVJ+elI9WAZUpZS2pZQagRXATR3m3AQ8mL1+GHh/tDUeuglYkVJqyJ6IUZWt1+ma2TnXZmuQrfkRgJTSEymlE9n4atqewAFwPfBoSqk2pVQHPEpbMUw5OXyyieqjDZRPHp13lFxdPnsChQXB2u3uopJ6UTPw+ymlC4GrgM928ksUgF+klJZkX3f3bURJkiRJ3elJgWoGsLvd+z3ZWKdzskaEh4HSU5zb1XgpcChbo6trQduuqp+cRj4i4vaIqIyIypqamk4/qM6NqupjACwY4gWqUcOKuHjGOJ7fVUdjs83Spd6QUno9pfRc9voobQ1wO/t7Q5IkSVI/1pMCVWePYEs9nHOuxn99oYhPABXAn51GPlJK96eUKlJKFWVlZZ2conNlS/VRRg8rYurY4XlHyd2yuRNpaG7lpT2H8o4iDXpZ/8PLgDWdHH5HRLwYET+JiMVdnO8vMiRJkqSc9KRAtQeY1e79TGBvV3MioggYB9Se4tyuxg8A47M13natiLgO+DJwY0qp4TTyqY+0pkRV9TEWTB5N2x2bQ9uc0pFMHjPMZulSL4uI0cAPgM+nlI50OPwcMCeldCnwV8APO1vDX2RIkiRJ+elJgWodUJ49Xa+EtqbnKzvMWQm88fS8jwKPZ49rXQncnD3lbx5QDqztas3snCeyNcjW/BFARFwGfJO24lR1u2s/AnwwIiZkzdE/mI0pB68frudEY8uQ7z/1hsiape+pO8lrdSfzjiMNShFRTFtx6jsppf/T8XhK6UhK6Vj2ehVQHBGT+jimJEmSpFPotkCV9YO6g7aiz0bgoZTS+oi4OyJuzKY9AJRGRBXwBeDO7Nz1wEPABuCnwGdTSi1drZmt9UXgC9lapdna0HZL32jgH7OnMK3MrlEL/CFtRa91wN3ZmHJQtf8oAOdZoHrTZbMmUFwYrN1xMO8o0qCTPVzjAWBjSukvupgzNZtHRCyj7e8+/4GUJEmS+pGi7qe8+RvnVR3G7mr3uh74WBfnfhX4ak/WzMa30faUv47j150i37eAb3X9CdRXth04zuQxwxg7vDjvKP3GiJJCLpk5nhd3H+aGi6YxvLgw70jSYHI18Eng5Yh4IRv7L8BsgJTSN2jblfu7EdEMnARuznbsSpIkSeonelSgknqipTWx4+BxrpgzMe8o/c6yuRN5dmcdL+w+xFXzS/OOIw0aKaWn6fxhGe3nfA34Wt8kkiRJknQmetKDSuqR1+pO0NSSmD9pVN5R+p2ZE0Ywfdxw1m6vxY0bkiRJkiS9lQUqnTPbDhwHYK4Fqrdpa5Zeyr4j9eyuPZF3HEmSJEmS+hULVDpnth84zpSxwxg9zDtHO3PpzHEMKypgzXZ7+EuSJEmS1J4FKp0Tb/SfmjfJp/d1ZVhxIUtmjefl1w5zorE57ziSJEmSJPUbFqh0Tth/qmeWzZtIc2vi+V2H8o4iSZIkSVK/YYFK54T9p3pm2rgRzJowgjU2S5ckSZIk6U0WqHRO2H+q566cV8qBYw1sz4p6kiRJkiQNdRaodNaaWlrtP3UaLp45juHFNkuXJEmSJOkNFqh01l7ac9j+U6ehuLCAK2ZPYMPeIxxrsFm6JEmSJEkWqHTWVm87CNh/6nQsnTuRlpR4dmdd3lEkSZIkScqdBSqdtdXbDtp/6jRNHjuceZNGsXb7QVptli5JkiRJGuIsUOmsNLW0Urmjzv5TZ2DZvInUnWiiqvpY3lEkSZIkScqVBSqdlZf2HOZkU4v9p87A4mljGVVSyFqbpUuSJEmShjgLVDor9p86c0WFBVwxZwKb9h1h3+H6vONIkiRJkpQbC1Q6K6u3HWThlDH2nzpDS+dOpDXB99ftzjuKJEmSJEm5sUClM/ZG/6mr5k/MO8qAVTp6GOWTR7Ni3S6aW1rzjiNJkiRJUi4sUOmMvdF/6sr5pXlHGdCWzZvI64freXxTdd5RJEmSJEnKhQUqnbHKHW3NvZfOdQfV2bhg6limjh3O3z+zI+8okiRJkiTlwgKVzti6HXXMmzSKsjHD8o4yoBUWBJ+6ei7PbD3I+r2H844jSZIkSVKfs0ClM9Lamnh2Zy0VcybkHWVQuGXpbEaWFPLAL7bnHUWSJEmSpD5ngUpnZNuBY9SdaPL2vnNk3MhifqtiFitf3Mu+w/V5x5EkSZIkqU9ZoNIZqdxRB8AVc91Bda78+6vn0ZISD/5qR95RJEmSJEnqUxaodEbW7ahj4qgS5k8alXeUQWN26UiWL57K/169k6P1TXnHkSRJkiSpz1ig0hmpzPpPRUTeUQaV37tmAUfrm/n26p15R5EkSZIkqc9YoNJpqz5Sz86DJ+w/1QsunjmO955fxgO/2M7Jxpa840iSJEmS1CcsUOm0Ve5s6z9VYf+pXnHHtQs4eLyR763dlXcUqd+LiFkR8UREbIyI9RHxnzqZExHxvyKiKiJeiojL88gqSZIkqWsWqHTaKnfUMayogMXTx+UdZVBaOnciV86byP1PbaOh2V1UUjeagd9PKV0IXAV8NiIWdZhzA1Cefd0O/HXfRpQkSZLUHQtUOm2VO2tZMms8JUX+8ekt//HacvYdqWfF2t15R5H6tZTS6yml57LXR4GNwIwO024C/iG1WQ2Mj4hpfRxVkiRJ0ilYYdBpOd7QzPq9R+w/1cuuXlDKO+aX8lePb+F4Q3PecaQBISLmApcBazocmgG0r/bu4e1FLCLi9oiojIjKmpqa3oopSZIkqRMWqHRaXth9iJbWZP+pXhYR/MHyhRw41si3nt6edxyp34uI0cAPgM+nlI50PNzJKeltAyndn1KqSClVlJWV9UZMSZIkSV2wQKXTUrmjjgi4fI4Fqt52+ewJfHDRFO5/ahu1xxvzjiP1WxFRTFtx6jsppf/TyZQ9wKx272cCe/simyRJkqSesUCl01K5s5aFU8Ywdnhx3lGGhP98/UKONzbzV49vyTuK1C9FRAAPABtTSn/RxbSVwL/NnuZ3FXA4pfR6n4WUJEmS1K0eFagiYnlEbM4e0X1nJ8eHRcT3s+Nrsj4gbxz7Uja+OSKu727NiJiXrbElW7MkG39PRDwXEc0R8dEO12+JiBeyr5Wn/21QTzS3tPLczjr7T/Wh86eM4eZls/mHX+1k076Ody1JAq4GPglc2+7vgQ9FxGci4jPZnFXANqAK+Bvg93LKKkmSJKkLRd1NiIhC4D7gA7TdJrEuIlamlDa0m3YbUJdSWhARNwP3AB/PHvV9M7AYmA48FhHnZ+d0teY9wL0ppRUR8Y1s7b8GdgGfAv5zJzFPppSWnOZn12natO8oxxtb7D/Vx/7ggwtZ9fLrfOVH61lx+1W0bRiRBJBSeprOe0y1n5OAz/ZNIkmSJElnoic7qJYBVSmlbSmlRmAFbY/sbu8m4MHs9cPA+7PbLm4CVqSUGlJK22n77fWyrtbMzrk2W4NszY8ApJR2pJReAlrP8LPqLFXuqAVwB1UfmzCqhD+4fiFrttey8kXb5kiSJEmSBp+eFKh68njuN+eklJqBw0DpKc7tarwUOJSt0dW1OjM8ezT46oj4SGcTfHz42Vu3s44Z40cwffyIvKMMOTcvnc1FM8byR/+8kUMnbJguSZIkSRpcelKg6snjubuac67GuzM7pVQB/DbwlxFx3tsW8fHhZyWlROWOWq7w6X25KCwI/uTfXELd8Ub+vx+tzzuOJEmSJEnnVE8KVD15PPebcyKiCBgH1J7i3K7GDwDjszW6utbbpJT2Zv+7DXgSuKz7j6XTsafuJPuPNLDU/lO5uWjGOD5/XTn/9OJeb/WTJEmSJA0q3TZJB9YB5RExD3iNtqbnv91hzkrgVuBXwEeBx1NKKXui3ncj4i9oa5JeDqylbafU29bMznkiW2NFtuaPThUuIiYAJ1JKDRExibYnOv1pDz6XTkPlzrb+U1fMsf9Unj7z3vN4bGM1/98PX2Hp3AlMG9f7t1t+d82uXr/G2fjtK2fnHUGSJEmSdJa63UGV9YO6A3gE2Ag8lFJaHxF3R8SN2bQHgNKIqAK+ANyZnbseeAjYAPwU+GxKqaWrNbO1vgh8IVurNFubiFgaEXuAjwHfjIg35l8IVEbEi8ATwJ90eMKgzoHKHXWMGVbEwqlj8o4ypBUVFvAXv3UpzS2t/D/ffpb6ppa8I0mSJEmSdNZ6soOKlNIqYFWHsbvava6nrXDU2blfBb7akzWz8W20PeWv4/g62m756zj+DHBxtx9CZ+XZnXUsmT2ewoJTPs1dfWB+2Wju/fgSbv/2s3z5/77Cn3/sEtoegClJkqSOImI58D+BQuBvU0p/0sW8jwL/CCxNKVX2YURJEj3rQaUh7vDJJjbvP8rSud7e1198cPFUPn9dOT94bg/3P7Ut7ziSJEn9UkQUAvcBNwCLgFsiYlEn88YAnwPW9G1CSdIbLFCpW8/tqiMlqPAJfv3K564t58OXTOOPf7KJf/jVjrzjSJIk9UfLgKqU0raUUiNtfW5v6mTeH9LWx7a+L8NJkn7NApW69eyOOgoLgiWzx+cdRe0UFAR/+fElXHfhFO760fp+38xckiQpBzOA3e3e78nG3hQRlwGzUko/PtVCEXF7RFRGRGVNTc25TypJQ5wFKnWrcmcti6ePZWRJj1qWqQ8VFxZw3+9cxjULy/gv//dl/vyRzbS2prxjSZIk9RedNep88z+WIqIAuBf4/e4WSindn1KqSClVlJWVncOIkiSwQKVuNLW08sLuQ1zh7X391rCiQu7/ZAUfr5jF156o4ne/8yxH6pvyjiVJktQf7AFmtXs/E9jb7v0Y4CLgyYjYAVwFrIyIij5LKEkCLFCpG+v3HqG+qZWKOTZI789Kigr4k9+8mLv+1SIe3bCf6+99iic2V+cdS5IkKW/rgPKImBcRJcDNwMo3DqaUDqeUJqWU5qaU5gKrgRt9ip8k9T0LVDqlyh21AFTMdQdVfxcR/Pt3zeP//N7VjB5WxL/7u3X83neepar6aN7RJEmScpFSagbuAB4BNgIPpZTWR8TdEXFjvukkSe3ZVEin9OzOOmZNHMGUscPzjqIeWjJrPD/+3Lv46ye38jdPbeOnr+zjxkunc+s757Jk1ngiOmvFIEmSNDillFYBqzqM3dXF3Gv6IpMk6e0sUKlLKSXW7ajj3eWT8o6i0zSsqJDPX3c+n7xqDt/4+Va+u2YXP3xhL4umjeWmJdO54aJpzC4dmXdMSZIkSZIAC1Q6hV21JzhwrMEG6QNY6ehhfPnDi/jc+8v50Qt7+f663fzxTzbxxz/ZxKJpY7nhoqnccPFUFkwek3dUSZIkSdIQZoFKXarcUQfA0rk2SB/oxgwv5hNXzeETV81hd+0JHlm/j5+8so//8eir/I9HX2XhlDH8q0um8eFLpjG/bHTecSVJkiRJQ4wFKnWpcmcdY4cXUT7ZgsVgMmviSD797vl8+t3z2X+knp++so8fv7T3zWLVomlj+TeXz+C3ls5i7PDivONKkiRJkoYAC1TqUuWOWi6fM4GCAptqD1ZTxg7n1nfO5dZ3zuX1wydZ9fI+Vr64lz/654385WNb+PjSWUwZO5zRw/xXhSRJkiSp9/hTpzp16EQjW6qPcdOS6XlHUR+ZNm4Et71rHre9ax4v7znMA09v4++f2UFRQfCe88t414JJFBcW5B1TkiRJkjQI+dOmOvXcrrb+UxX2nxqSLp45jr+8+TIe+fx7mF82mkc37Odrj1exp+5E3tEkSZIkSYOQBSp1qnJHHUUFwaUzx+cdRTlaMHk0n7xqDv/u6rk0trTyjZ9v5fFN+2lNKe9okiRJkqRBxAKVOlW5o47FM8YxoqQw7yjqB8onj+Fz15ZzyczxPLaxmu+t3UVjc2vesSRJkiRJg4QFKr1NY3MrL+45RMWcCXlHUT8yoqSQj10xkxsumsqGvUe4/6mtHGtozjuWhriI+FZEVEfEK10cGnVWPQAAIABJREFUvyYiDkfEC9nXXX2dUZIkSVL3LFDpbV7Ze5iG5laWzrVApbeKCN5dXsYn3zGHmmMN/O0vtnG0vinvWBra/h5Y3s2cX6SUlmRfd/dBJkmSJEmnyQKV3qZyRy0AV8yxQbo6d8HUsdz6jrnUnWjkb3+x3SKVcpNSegqozTuHJEmSpLNjgUpvU7mjjjmlIykbMyzvKOrH5peN5lPvnMfhk008+KsdNDS35B1J6so7IuLFiPhJRCzualJE3B4RlRFRWVNT05f5JEmSpCHPApXeIqXEszvrqHD3lHpg3qRR3LJsFq8fqmfF2t20tPp0P/U7zwFzUkqXAn8F/LCriSml+1NKFSmlirKysj4LKEmSJMkClTrYcfAEB483UmH/KfXQwqljuXHJdDbvP8qql1/PO470FimlIymlY9nrVUBxREzKOZYkSZKkDixQ6S3WZf2nfIKfTseV80p514JJ/GrbQV7YfSjvONKbImJqRET2ehltf+8dzDeVJEmSpI6K8g6g/uXZHXWMG1HMeWWj846iAeb6xVPZU3eCHz7/GtPGDWfK2OF5R9IQEBHfA64BJkXEHuArQDFASukbwEeB342IZuAkcHNKyXtRJUmSpH7GApXeonJnLRVzJlBQEHlH0QBTWBDcvHQ2f/VEFd9du4vPXrOAkiI3aap3pZRu6eb414Cv9VEcSZIkSWfInx71poPHGthac5wr7D+lMzR2RDEfr5hFzdEGfrreflSSJEmSpJ6xQKU3rd3e1n/qynmlOSfRQLZg8miuPq+U1dtqqao+lnccSZIkSdIAYIFKb1q97SAjigu5ZOa4vKNogPvg4qlMGj2MHzy3h/qmlrzjSJIkSZL6OQtUetOa7bVUzJ1AcaF/LHR2igsL+NgVMzla38Sql73VT5IkSZJ0alYiBEDd8UY27TvKlfMm5h1Fg8SsiSO5esEkKnfWsePA8bzjSJIkSZL6MQtUAmDtjqz/1Hz7T+ncef8FUxg/spgfvvAaza2teceRJEmSJPVTFqgEwJpttQwrKrD/lM6pkqICbrxkOtVHG3h6y4G840iSJEmS+qkeFagiYnlEbI6Iqoi4s5PjwyLi+9nxNRExt92xL2XjmyPi+u7WjIh52RpbsjVLsvH3RMRzEdEcER/tcP1bs/lbIuLW0/82aPW2g1wxZwLDigrzjqJB5oJpY1k8fSxPbK7m0InGvONIkiRJkvqhbgtUEVEI3AfcACwCbomIRR2m3QbUpZQWAPcC92TnLgJuBhYDy4GvR0RhN2veA9ybUioH6rK1AXYBnwK+2yHfROArwJXAMuArETGhp98AweETTWzcd4Qr53l7n3rHhy+eRkrwk1f25R1FkiRJktQP9WQH1TKgKqW0LaXUCKwAbuow5ybgwez1w8D7IyKy8RUppYaU0nagKluv0zWzc67N1iBb8yMAKaUdKaWXgI6NbK4HHk0p1aaU6oBHaSuGqYfW7aglJbhyvg3S1TvGjyzhPeeX8fJrh9luw3RJkiRJUgc9KVDNAHa3e78nG+t0TkqpGTgMlJ7i3K7GS4FD2RpdXetM8hERt0dEZURU1tTUdLPk0LJm+0FKigpYMmt83lE0iL2nvIxxI4r58Ut7aU0p7ziSJEmSpH6kJwWq6GSs40+XXc05V+On0qNzUkr3p5QqUkoVZWVl3Sw5tKzeVsuSWeMZXmz/KfWekqICbrhoKq8frue5nXV5x5EkSZIk9SM9KVDtAWa1ez8T2NvVnIgoAsYBtac4t6vxA8D4bI2urnUm+dSFI/VNrN97mKvm239Kve/iGeOYOWEEP9tUTVNLx7t1JUmSJElDVU8KVOuA8uzpeiW0NT1f2WHOSuCNp+d9FHg8pZSy8Zuzp/zNA8qBtV2tmZ3zRLYG2Zo/6ibfI8AHI2JC1hz9g9mYeuDZHXW0Jrhqnv2n1PsiguWLp3L4ZBOrtx3MO44kSZIkqZ/otkCV9YO6g7aiz0bgoZTS+oi4OyJuzKY9AJRGRBXwBeDO7Nz1wEPABuCnwGdTSi1drZmt9UXgC9lapdnaRMTSiNgDfAz4ZkSsz65RC/whbUWvdcDd2Zh6YPX2gxQXBpfN9sGH6hvzy0Zz/pTRPLm5hpONLXnHkSRJkiT1A0XdT4GU0ipgVYexu9q9rqetcNTZuV8FvtqTNbPxbbQ95a/j+Drabt/r7BrfAr51yg+hTq3ZVsulM8czosT+U+o71y+eytcer+KpLTVcv3hq3nEkSZIkSTnryS1+GqSONTTz8muHuXK+t/epb00bN4JLZ43nma0HOHyyKe84kiRJkqScWaAawp7dWUdLa+LKeTZIV9+77sIptLbC45v25x1FkiRJkpQzC1RD2JptBykqCK6YY/8p9b2Jo0pYNn8iz+6so/pofd5xJEmSJEk5skA1hP1q20EunjmOUcN61IpMOufet3AyxYUFPLrBXVSSJEmSNJRZoBqijtQ38eLuQ7xrwaS8o2gIGz2siHctmMT6vUd4re5k3nEkSZIkSTmxQDVErd56kNYEV1ugUs6uXjCJEcWFPLbRXVSSJEmSNFRZoBqifll1gBHFhVw2e3zeUTTEDS8u5D3lk9i8/yi7Dh7PO44kSZIkKQcWqIaop6sOcOX8iQwrKsw7isRV55UyqqSQxzZW5x1FkiRJkpQDu2MPQa8fPsnWmuPcsmx23lGU+e6aXXlHyNWwokLee34Zq17Zx/YDx5k3aVTekSRJkiRJfcgdVEPQL6sOAvafUv9y5fxSxgwv4tEN+0kp5R1HA0hEfCsiqiPilS6OR0T8r4ioioiXIuLyvs4oSZIk6dQsUA1Bv6w6wKTRJSycMibvKNKbigsLuOb8MnYcPM7WGntR6bT8PbD8FMdvAMqzr9uBv+6DTJIkSZJOgwWqISalxNNVB3jneZMoKIi840hvsXTuRMaNKObRDfvcRaUeSyk9BdSeYspNwD+kNquB8RExrW/SSZIkSeoJC1RDzKZ9R6k52sC7vL1P/VBRYQHvWziZ3XUneXX/0bzjaPCYAexu935PNvYWEXF7RFRGRGVNTU2fhZMkSZJkgWrI+fmrbT90vXdhWc5JpM5dMWcCE0YW89jGandR6VzpbLvo2/5wpZTuTylVpJQqysr8d6QkSZLUlyxQDTFPbq7mgqljmDJ2eN5RpE4VFgTXXjCF1w6dZOPrR/KOo8FhDzCr3fuZwN6cskiSJEnqhAWqIeRofROVO+q4ZuHkvKNIp7Rk1ngmjS7hsY3VtLqLSmdvJfBvs6f5XQUcTim9nncoSZIkSb9mgWoIeWbrQZpbE+8931tX1L+9sYtq35F6XnntcN5x1M9FxPeAXwELI2JPRNwWEZ+JiM9kU1YB24Aq4G+A38spqiQpBxGxPCI2R0RVRNzZyfEvRMSGiHgpIn4WEXPyyClJQ11R3gHUd57cXMPoYUVUzJ2QdxSpW5fMHMeTm6v52aZqLpoxjoLwqZPqXErplm6OJ+CzfRRHktSPREQhcB/wAdpu+V4XEStTShvaTXseqEgpnYiI3wX+FPh436eVpKHNHVRDREqJn2+u5uoFpRQX+n+7+r+CCN5/4RRqjjbw4u5DeceRJEkD0zKgKqW0LaXUCKwAbmo/IaX0RErpRPZ2NW29CiVJfcxKxRBRVX2MvYfree/59p/SwLF4+limjRvO45uqaWm1F5UkSTptM4Dd7d7vyca6chvwk84ORMTtEVEZEZU1NTXnMKIkCSxQDRmPb6oG4L0L7T+lgaMggusunMLB4408v6su7ziSJGng6axHQKe/9YqITwAVwJ91djyldH9KqSKlVFFW5n9TS9K5ZoFqiHhs434WTRvLjPEj8o4inZYLpo5h5oQRPL65mubW1rzjSJKkgWUPMKvd+5nA3o6TIuI64MvAjSmlhj7KJklqxwLVEHDwWAPP7qzjukVT8o4inbbIdlEdOtHEszvdRSVJ0tmoPd7I9fc+xaqXX887Sl9ZB5RHxLyIKAFuBla2nxARlwHfpK04VZ1DRkkSFqiGhMc3VdOa4AMXWqDSwFQ+eTSzJ47kiU3VNLW4i0qSpDN1vKGZzfuPcryhOe8ofSKl1AzcATwCbAQeSimtj4i7I+LGbNqfAaOBf4yIFyJiZRfLSZJ6UVHeAdT7Htu4n6ljh3PRjLF5R5HOSETwgUVTeODp7azbUcs7z5uUdyRJkgakE40tAIwsGTo/BqSUVgGrOozd1e71dX0eSpL0Nu6gGuTqm1p46tUDXLdoMhGd9YiUBobzykYzb9IontxcQ2Ozu6gkSToTJxrbdk6NKPHHAElS/+LfTIPcM1sPcLKphQ8smpp3FOmsfeDCKRxraGbN9oN5R5EkaUA6me2gGlE8dHZQSZIGBgtUg9yjG6oZVVLIVfMn5h1FOmtzJ42ifPJofv5qDQ1NLXnHkSRpwDnZ9MYtfoU5J5Ek6a0sUA1izS2tPLphH9dcMJlhRf5HiAaH6y6cwonGFn61zV1UkiSdrl/3oPK/DSVJ/YsFqkFs7fZaDhxr5MMXT8s7inTOzJo4kgumjuEXWw5Q7y4qSZJOy5u3+FmgkiT1MxaoBrF/fvl1RhQX8r6Fk/OOIp1T1104hZNNLTxddSDvKFK/8UzVAfYdrs87hqR+7s0m6cUWqCRJ/YsFqkGquaWVn76yj2svnOxvyDToTB8/gsXTx/LLqgMcPNaQdxypX/j0P1TyrV9uzzuGpH7uxJs9qGySLknqXyxQDVJrt9dy8Li392nw+sCFU2hqaeV//mxL3lGk3DU0t3CisYXDJ5ryjiKpn6tvbCEChhf7Y4AkqX/p0d9MEbE8IjZHRFVE3NnJ8WER8f3s+JqImNvu2Jey8c0RcX13a0bEvGyNLdmaJae6RkTMjYiTEfFC9vWNM/1mDCY/9vY+DXKTxw5n6dyJfGfNLqqqj+YdR8rV8Ya2HRHHs1t3JKkrJxpbGFFcSETkHUWSpLfotkAVEYXAfcANwCLglohY1GHabUBdSmkBcC9wT3buIuBmYDGwHPh6RBR2s+Y9wL0ppXKgLlu7y2tktqaUlmRfnzmt78Ag1NzSyiPe3qch4P0XTmFkcSF/vGpT3lGkXB1vaH7L/0pSV040tdh/SpLUL/VkB9UyoCqltC2l1AisAG7qMOcm4MHs9cPA+6Pt1zI3AStSSg0ppe1AVbZep2tm51ybrUG25ke6uYY6eLrqAAePN/Ibl3h7nwa30cOKuOPaBfxsUzVPb7Fhuoauo/VvFKh8sqWkUzvZ2OIvMCVJ/VJPClQzgN3t3u/Jxjqdk1JqBg4Dpac4t6vxUuBQtkbHa3V1DYB5EfF8RPw8It7dg880qP3gudcYP7KY913g7X0a/G5951xmThjBH/3zBlpaU95xpFwce2MHlbf4SerGycYWRlqgkiT1Qz0pUHW2S6njT4FdzTlX46e6xuvA7JTSZcAXgO9GxNiOEyPi9oiojIjKmpqaTpYaHI7UN/Ev6/fxG5dMZ1iR//GhwW94cSF33nABm/Yd5eFnd3d/gjQIHWtoa47uLX6SunOiqYURPsFPktQP9aRAtQeY1e79TGBvV3MioggYB9Se4tyuxg8A47M1Ol6r02tktw8eBEgpPQtsBc7v+CFSSvenlCpSShVlZWU9+NgD009efp2G5lb+zeUdN7lJg9eHL57G5bPH8+f/8qo/oGtIOpbd2nfMW/wkdeNkYzMj7UElSeqHelKgWgeUZ0/XK6Gt6fnKDnNWArdmrz8KPJ5SStn4zdkT+OYB5cDartbMznkiW4NszR+d6hoRUZY1XSci5mfX2Nbzb8Hg8oNnX2N+2SiWzBqfdxSpz0QE//VfLaLmaAPf+PnWvONIfe5Y1oPqhLf4SerGCXtQSZL6qW4LVFm/pzuAR4CNwEMppfURcXdE3JhNewAojYgq2m6zuzM7dz3wELAB+Cnw2ZRSS1drZmt9EfhCtlZptnaX1wDeA7wUES/S1jz9Myml2jP7dgxsuw6eYO2OWn7z8pk+OlhDzuWzJ3DjpdP55lPb2HnweN5xpD71xi1+JxpbaLUXm6RTONlkgUqS1D/16Ab0lNIqYFWHsbvava4HPtbFuV8FvtqTNbPxbbQ95a/jeKfXSCn9APhBtx9iCHj42d1EwEcu8/Y+DU1f/vCFPL6pmq+sXM/ffWqphVoNGW/soIK2RuljhhfnmEZSf3ayscVb/CRJ/VJPbvHTANDU0sqKdbu55vwyZowfkXccKRdTxg7n89eV8+TmGv5lw/6846iPRMTyiNgcEVURcWcnxz8VETUR8UL29ek8cvam9r2nTjTah0pS1074FD9JUj9lgWqQeHTDfqqPNvCJq+bkHUXK1a3vnMvCKWO4+5822DB9CMh6EN4H3AAsAm6JiEWdTP1+SmlJ9vW3fRqyD7xxi1/ba//cS+raycYWhlugkiT1QxaoBon/vXonM8aP4JqFk/OOIuWquLCAP/rXF/HaoZP8xaOv5h1HvW8ZUJVS2pZSagRWADflnKnPtS9KWZiV1JXmllYaW1oZWdyjLh+SJPUpC1SDwNaaYzyz9SC/feVsCgvsuSMtnTuRT1w1m7/75XZe2H0o7zjqXTOA3e3e78nGOvrNiHgpIh6OiFmdLRQRt0dEZURU1tTU9EbWXnO0fQ+qBm/xGyyeqTrApx+spMXG9zpHTja1/fvBW/wkSf2RBapB4Durd1FcGHx8aac/c0lD0v+7/AImjxnOnT94iaaW1rzjqPd0VpXv+NP8PwFzU0qXAI8BD3a2UErp/pRSRUqpoqys7BzH7F3HG5oZN6L4zdcaHJ6uOsBjG/ezp+5E3lE0SJzMetT5FD9JUn9kgWqAO1LfxD9W7uZDF09j0uhheceR+o2xw4v5w49cxKZ9R/n6E1vzjqPeswdoX52fCf9/e3ceJldV53/8/e2u6up9Szr7vhvCFsKurAoICCiLIKIs6uCI6Iw6I+Ljz23GjdFRQNBBBRlZdcSIrLLIkgQSloSQnc6+dTrp9N5VXVXn98e9HSpNd1KdXmr7vJ6nnq671L3nnHv7fuueOudctiWu4Jzb7ZwL+5P/AxwzRGkbMi3hKCPLvRjQGlEFVbbY2+6NLbauriXFKZFs0fUQBbWgEhGRdKQKqgz3h0WbaA5H+ewHpqQ6KSJp50OzR3LhUWO49dm1vLWlMdXJkcGxGJhuZpPNrAC4HJifuIKZjU6YvABYOYTpGxItHVFGlhcC6uKXTfa2RQBYqwoqGSBdFVRFQVVQiYhI+lEFVQYLR2P89uX1fGD6cOaMrUh1ckTS0ncvmMOw0gL+9aE36ejUjXu2cc5FgRuAJ/Eqnh5yzr1tZt81swv81W40s7fNbClwI3B1alI7eJrDiRVUakGVLfa2eS2o1u5UBZUMjK4xqNTFT0RE0pEqqDLYn1/fyq7mMNefOjXVSRFJWxXFQX58yZGsrWvhJ0+uTnVyZBA45x5zzs1wzk11zv2HP+9bzrn5/vubnHOHOeeOdM6d7pxbldoUDyznHK3hKCPKvC5+LaqgyhpdFVTr6ppTnBLJFu37uvjpKX4iIpJ+VEGVoWJxx69fqGXO2HJOmjos1ckRSWunzqjhqhMm8puX1vPc6rpUJ0dkQLV3xog7KC8KUhTMp01jUGWNxoQxqJzTk/yk/7quDxqDSkRE0pEqqDLUX5duo7a+lc+fOg2znh5iJSKJbj7vfcwaVcZXHlrKzqaOVCdHZMC0dHg3nKWhACWhAC0agyprNLRFKCnIpzUSY3ujrlvSf11d/Ao1BpWIiKQhVVBloEg0zk+fXsPs0eV8eM6oVCdHJCMUBvO57RNzaY/E+NIDbxCLqzWCZIdmv0tfWWGAklC+xqDKEuFojLZIjLkTqwANlC4DQ0/xExGRdKYKqgz04JLNbNrTxtfOnklenlpPiSRr2ohSvnfRHBbV7tF4VJI1uiqkSkMBSgoC6uKXJbq6982bWA143fxE+qtdFVQiIpLGVEGVYdoiUX7xzFqOnVTFaTNrUp0ckYxzyTHjuPL4Cdz5j3d4dNm2VCdHpN+6uviVhAKUhgIaJD1LdA2QPqWmhOqSAg2ULgNCT/ETEZF0pgqqDPObF9ezqznMv50zS2NPiRyi//eRwzhmYhVfe3gZK7c3pTo5Iv3SnNCCqjiUv68Lj2S2rgqqquICptWUsnanWlBJ/7VFouQZFOTrFkBERNKPolMG2bynjdueW8c5h43i2EnVqU6OSMYqCORxx5VzKS8KcN3dizVoumS0rhZU3hhUakGVLfa2RQCoLA4ybWQpa/UkPxkAbZEYxQUB/cgpIiJpSRVUGeQ7f32b/DzjWx+ZneqkiGS8EeWF/PbqY2ls7+Sa3y3WTb1krNbIuy2oSgsCGiQ9S+z1x6CqKAoyfUQpje2d1LdEUpwqyXQdnTF17xMRkbSlCqoM8fSKnfx9ZR1fOnM6YyqLUp0ckaxw2JgKbr9yLqt3NvPPf3idcFRdoyTzNPstqEoL/S5+YZ3H2WC/FlQjSgFYq3GopJ+8FlSqoBIRkfSkCqoM0NjWyf/7y3Kmjyjl2vdPTnVyRLLKaTNH8IOPHs4La3bxpfvfJBqLpzpJIn3SEo4SzDdCgXxKQwFaI1F1BcsCe9s6CeQZpaEAU2u8CqraXa0pTpVkurZIjKKgKqhERCQ9BVKdADkw5xzf/MtydjaH+dMnjyGoQS1FBtxlx46nJRzlu4+u4KsPL+W/LjuK/DyNzyHp6b5XNu03/frGhn2xoSQUIO68J3UVFyjEZ5rEY7tkQwOhYD73v7qZy48dTzDf2NLQnsLUSSbqfr1Yv6tVrYVFRCRt6dtrmnvkza38dek2vnrWDI4aX5nq5IhkrWvfP5n2zhg/eXI10bjjZx8/ShXCkhEi0TihgF9B5XfdaQ2rgirTtXXGKPZbuuTlGWMri9jc0JbiVEmmi8TiFAQU20REJD3p22saW1/fyrceeZtjJ1Xx+dOmpTo5IlnvC6dPIz/P+OHjq2iPxLj9yrkUqiuEpLmOaHzfeVoS8sJ6azhKTVkolcmSfmqLRPcbK2h8dbFaUEm/dcbilBQEU50MERGRHuknlDTV2N7JdfcsJpBv/FTdjUSGzPWnTuX7F83h2dV1fPKuV9jdEk51kkQOKNwZ29cioquCSk+lzHztkf2ftjauqogte9SCSvonEo0TVAsqERFJU4pQaSgai3PDfa+zeU8bd37yGMZXF6c6SSI55ZMnTOS2K+by1tZGPvrLBayra0l1kkR6FY7GKQz4Laj8bn1tEY0xk+nauz1tbVxVMbtbI7RFVPkohy4Si1Og7usiIpKmFKHSTDzu+Maf3+LFtfX8x0WHc/yUYalOkkhOOu+I0dz/uRNoi0T56O0v8/hb21OdJJEehaMxQsGuFlRdY1CpEiPTdX/a2riqIgB185N+UQsqERFJZ4pQaSQed9z8yFs8tGQLN54xjcuOHZ/qJInktLkTqvjLDe9nyohSPv+H1/nOX9/W048k7YQ744T8FlSl6uKXFaKxOJFYnOLQu0OFdrWm3qKB0qUfOtWCSkRE0pgiVJqIxuLc/Mhb3P/qZr5w+lT+5UMzUp0kEQHGVhbx8D+dyNUnTeJ3L2/ggltfZvnWxlQnSzJAQ1sE59yg76cjGqPQbxHRVaGhbmCZrb3TqwjvqQXV5j1qQSWHJhqPE3foKX4iIpK2FKHSQFNHJ9fes2Rf5dRXz5qJmQZFF0kXBYE8vn3BYfzu6mPZ2x7hottf5sdPrFIlgPRqV3OYW55czVuDXJkZizs6Y44Cv4tfaUFXCyq19MtkXWOIJY5BVVMaIhTIY7MGSpdD1Bn1KszVgkpERNKVIlSKrd3ZzMW/XMCCdfX84GOH87WzZ6lySiRNnT5rBE99+VQuOnosv3z+Hc78r38wf+k24vHBbyUjmWVdXTMOWLNzcAfYj0TjAPsGSS9OGIPKOcfrmxqGpBWXDKx3K6je7eJnZt6T/DQGlRyiSMy7XgRVQSUiImlKESpF4nHHXS/Wct6tL7G7NcLvrz2OK46bkOpkichBVBQHueXSI/nj9SdSVVzAjfe/wfm3vsQzK3eqIkD2qa1v9f8ObgVV15hoIb/LTjA/j4JAHq2RKH9fWcfHfrmAZ1fVDWoaZOC1+xVURQktqMAbh2qzxqCSPtq2t51dzWE6/QrtgoB+CBURkfSkCqoUeG1jAxffuYDv/20lp0wfzpNfPoWTpg1PdbJEpA/mTarmr198Pz/7+JG0hKNcd88SPvzzF/nja1s0kHqOi8cdtbtaCeYbe9s62dMaGbR9dfg3nKGEsYpKQwFaw1Ee8588+dhbOwZt/zI42ju97sPFwf0rqNSCSvoqFnfcs2ADDyzetK8Flbr4iYhIulKEGkLLtzbyhT+8zsV3LGBLQzu3XHok//OpedSUhVKdNBE5BPl5xkePHsczXzmVn1xyBM7BVx9eyok/eJbvPbqCVTua1KoqB63a0Ux7Z4wTp3g/PNTu8lpRbd7TxtceXkqnf5M4EMKd+7egAigJ5bO3rZNnVu4E4JlVOwd0nzL42nprQVVVTGN7J00dnalIlmSgd3a10ByOsr2xY1/ru6AGSRcRkTQVOPgq0h/tkRhPrdjBA69uZmHtbkpDAW48Yxr/dOpUSkIqfpFsEMzP49J547nkmHG8uLaeBxZv4vcLN/Cbl9YztaaEcw8fzSkzajhyXKWenpQDFtXuBuCEKdW8tnEPtfWtHDOxikeXbWNzQzvHTq7msnnjAW8wdeCgP1S0RaL7jUf02sYGXlm/m/C+MagSKqgKAry4tp6mjiiXzRvHQ0u28Or6PZw8bTi1u1pYsb2J848YM6B5lv6ra+7gvlc3ccasEbRFYuTZ/hWPAOOqigHYsqed2WOCqUimpLk1O5t5aW0915w8CTPjzc17CQXy6IzFWbx+D6AWVCIikr5UQzIIdjZ18OLaep5fXcdzq+pojcQYW1nEN86dxeXHTaC8UF8qRbKRmXHKjBpwAuNuAAAYvUlEQVROmVHD7pYwjy3fwWPLtnP7c+u49dl1FAXzmTepihOnDuP4ydXMGlWuiuostLB2N9UlBVQWFzC5ppT19a2sq2thc0M7hcE8bn9uHR87eiwt4SgfufUlAJ788ilUFAfZ1Rzmhvte58oTJnLBkV4l0l0v1vLjJ1bzu2uO5eRpw6lvCfPIm1upLi6gsjjIiLIQIysK9+2/JBSgsb2T4oJ8vnHu+5i/dBtPLN/B3AlVXHv3YjbsbqOsMMipM2oAr/KrID+PgG5aU+o//7aS5Vsb2dsWYXRFIUUFgfc8NGV8dREAmxvamD2mPBXJlDTinGPl9mZmjiojP8/o6Ixx/b2vUVvfSkVRkHPmjOLtbY0cNb6KpvZOVu9sBjRIuoiIpK+k7ozM7Bzg50A+cJdz7ofdloeA3wPHALuBjzvnNvjLbgKuA2LAjc65Jw+0TTObDDwAVAOvA1c55yKHso/B5JyjqSPKzqYONtS3srauhZXbm3hj01627vXGh6gpC/GRI8dw0dFjOW5SNXl5GpRSJFcMKw1x1QkTueqEiexti7Codg8L36lnYe1ufvzE6n3rja8uYubIcmaNKmPy8BJGVxYypqKIURWFFHYbf0Z61p8YNdBicccrtbuZMbIMgCnDS1i+tZH5S7dRURTk+xfN4Yv3v8Ffl23j7yvrqG/xWlDd/Mhb/OzjR/GFP7zOqxv28MbmvUysLibmHD98fBUx5/jyg2/y+Jc+wPyl2wjkGZ/5wGTKevjBo6vS8/SZI6gsLuC0GSN4asUOCgJ5bNjdxsjyEDf9aRlP/sspbNrTxqd/+yqjKgq555rjGFYaoi0S5a9Lt3H6rBGMKPMqvrq6quopswMjHI1x5/O1TBhWxEVHjWXhO7t55M1tTKguZtOeNnY1h3v8MWtfCyqNQ5X1ItE4kVicUv//uTMW596FG5lcU8LpM0fgnOM//raSu15az0ePHsstlx7Jrc+upba+lQnVxXzvbyvY3RqmM+Y4enwlTR3vVlDlYkvedIoTIiLSu4NWUJlZPnA78CFgC7DYzOY751YkrHYd0OCcm2ZmlwM/Aj5uZrOBy4HDgDHA381shv+Z3rb5I+BnzrkHzOxOf9t39HUfzrkBH6X4+dV13PmPd9jZFGZHYwftnfvvYkxFIUdNqOTqkyZx4tRhzB5drkopEaGyuIBz5ozinDmjAK9b1xubGli9o5lVO5tZvaOZ51bXEYvvP15VdUkBVcVByouClBcGqSgKUloYoCA/j2C+EfRbvRgQjcfpjDk6Y3GiMccpM2r40OyRKcjt0OpPjBqM9Kzc3kRTR5QpNSUA+/7ubo3wkSNGc97ho7n9uXV86y9v09wR5Wtnz8Q5xy1PrWHb3nZe37SXb39kNv/z4nqu/9/XyM8zRpYX8tPLjuSq377KxXcsYOPuNj5yxOgeK6cASvxxi846zDv+Z88ZyRNv7+A3L63nE8dP4JJjxnHxHQu48f43WLKxgeKCfNbVtXDprxbypTOnc8tTq9m8p53ywgDfPG82VSUF3PbcOlZub+Lj88ZzzcmTWLWjmT+/sRUDPjZ3LMdOqmbBO7tZ8E49k4aV8MHZIykLBVhYu5t3drVy5LgKjp1cze6WCG9saqCjM87REyqZUlPC+vpWVm5vorKogMPGllMWCrJmZzNbGtoZX13E9BFlhKMx1ta10NIRZWpNKWOritjZ1MH6+lZCgTym1pRSWhhg8542tjS0M6I8xKRhJcTijg27W2ls62R8dTFjKotoaIuwvr4VAyYPL6GiKMiWhnY27mmjurhg3zFbW9dCXVMHk4aXMGlYCfUtYVZsayISizN7dDmjKwt5e1sTSzfvZVhpiGMmVhHIM55bVceyrY0cNb6SU2fUsHTzXu5ZuIEdjR1cOm88x02u5pt/Xs6K7U0APLOyjpXbm5hQXcx175/M3Qs2sL6+leKC91ZQVxUHKSnIZ82OZpZs2MOu5jAThhUztaZ0X4V2ZyzOzqYOGts7qSkLMbwkRGskyta97URjjrGVRVQWB9ndGmFHYwfFBfmMqSwikGfsagmzuyXC8NIQNWWhfdsKR+OMLCukvChAczhKXVMHBfn5jCgPUZCfR0NbhD2tESqKggwrDRF3jl3NYdoiMWpKQ5QXBWjvjFHXFCbPjBHlIUKBPPa2dbK7NUxpKMjw0gIA6lsiNHV0Mrw0RFVxkI7OODuaOojFHaMqCikp8MZY29ncQUlBgBHlIfLN2NkcpqE1wrDSAkaUFRKOxtja0E5HZ5wxlYVUlxRQ3xJhS0MboUA+46uLKAzms6Whne17vXNmfHUx4Wic2l2tNLZ3MnlYCWMqC9nR1MGanc3kmTFzVBlVxQWs3tHMmp3NjKoo5PCxFXTGHK9tbGBLQxuHjangiHEVrK9v5aV19XRG45w0bTjTakp5fk0dz6/exbiqIj48ZzT5ecZ9r2xkUe0eTptZw6XzxrGodg+3PruWhtZOPnH8BM47YjTfe3QFy7Y0AnDFceMJBfK5e8EG5k6o5M9vbKWhLcJLa+u5eO44Pn/aFM79+Uv852OrqCoOMnFYMdG4IxTIIxyN51wXv3SLEyIi0rtkWlAdB6xzztUCmNkDwIVA4kX9QuDb/vs/AreZ9zPrhcADzrkwsN7M1vnbo6dtmtlK4AzgE/469/jbveMQ9rEwyTJImnMQjTlmjynnjFkjGFVeyMiKQsZVFTF9RGmvNwsiIolqykKcddgozjps1L554WiMbXs72L63nW2N3t/tTR00tnXS2N5JQ1uEjbtbaQlHiUS9yqiuSimAQJ4RyDeCeXkEA3mMqijMiQoq+hGj3CCMYN81/tTk4aUA1JSGKPNbQMzzW9J+8YzpfOG+15k3sYrrT50KwPOrd7FkYwOf/cBkrj55MvMmVXPJnQvojDkevv5E5k6o4lvnz+abjyxnTGUhx08Z1msaygoDBPON02eNAOCMWSMJ5Bk1ZSFu+vAsygqDXHfyZO56aT1Thpdw72eOZ2tDO9fdvZgvPfAmk4eX8IsrjuZ/F27k3/60DPBa+n14zigeWLyJexdtBGBkeQjn4KkVO/ftuzQUoCUc5QePrxqwMjXz4m9f5/W0Tp5BPIl5yaThQIqC+dz3yqZ906P97wo/9MulqjjIr686hrV1Lfz06TXE4o7fXX0s2xs7uODIMdz67NoeK6jMjPHVxTy4ZDMPLtm837JgvpGfZ4Sj8f3Smp9n76n8TqYcevpcIM+IdpvXfb2ett3T57rP62qcl5j2ZD7X0z6TPc7d5yV7rvWVGfzX02v2TVcVB2ls7+TWZ9cB3phQR46v4K6X1vOrF2oBmDexig9ML+HeRRu5e8EGKouD/OKKo1mxrYlfvfAOzsE1J0/iW+fP5o5/vMOPn1jNsJICvnne+6gqKeCGM6bx06fXcNT4KsyMYL5x+NgKlmxsyMUWVGkVJ0REpHd2sOuumV0CnOOc+4w/fRVwvHPuhoR1lvvrbPGn3wGOx7vQL3LO/a8//zfA4/7H3rPNhPWn+fPHA4875+b0dR/OuT92y8fngM/5kzOB1WS24UB9qhORYioDlQGoDLoMZDlMdM7VDNC2BlV/YpRzrr7btgYqTuTaOZlL+c2lvILym836m1fFCZgDLB+CLKS7XPq/ORCVg8qgi8oBZjrnyg7lg8m0oOqpj1r3Wq3e1ultfk8/3Rxo/UPZx/4znPs18Ose1s1IZrbEOTcv1elIJZWBygBUBl1yuBz6E6P2nzFAcSLXjkUu5TeX8grKbzbLpbwySHEix8qwVyoHj8pBZdBF5eCVwaF+Npk2vluA8QnT44Btva1jZgGgAthzgM/2Nr8eqPS30X1ffd2HiIhkv/7EKBERyX6KEyIiGSKZCqrFwHQzm2xmBXgDks/vts584NP++0uAZ/0+2/OBy80s5D+dbzrwam/b9D/znL8N/G3+5RD3ISIi2a8/MUpERLKf4oSISIY4aBc/51zUzG4AnsR7NOtvnXNvm9l3gSXOufnAb4B7/QHK9+Bd+PHXewhvEMIo8IWup+v1tE1/l/8OPGBm3wfe8LfNoewjy2VNd8V+UBmoDEBl0CUny6E/MWoQ5dqxyKX85lJeQfnNZjmT10GMEzlThgehcvCoHFQGXVQO/SiDgw6SLiIiIiIiIiIiMphy7jmzIiIiIiIiIiKSXlRBJSIiIiIiIiIiKaUKqjRgZvlm9oaZPepPTzazV8xsrZk96A/oiD8Q/INmts5fPilhGzf581eb2dkJ88/x560zs68Pdd6SYWaVZvZHM1tlZivN7EQzqzazp/0yeNrMqvx1zcx+4ednmZnNTdjOp/3115rZpxPmH2Nmb/mf+YWZ9fQo4ZQys38xs7fNbLmZ3W9mhblwHpjZb82szsyWJ8wb9GPf2z5SoZcy+In//7DMzP5sZpUJy/p0jA/lPJLkHOz/KtvKOIn8/quZrfDP22fMbGIq0jkQkr1mmtklZubMLKMfJ51Mfs3sMv/4vm1m9w11GgdKEufxBDN7zrzvZcvM7NxUpHMg9BRfui3vNa7Ku3LtWt+bXIoBvcm12NCbXIoZB5JL8aQ3gxJnnHN6pfgF/CtwH/CoP/0QcLn//k7g8/77fwbu9N9fDjzov58NLAVCwGTgHbxBIPP991OAAn+d2anObw/5vwf4jP++AKgEfgx83Z/3deBH/vtzgccBA04AXvHnVwO1/t8q/32Vv+xV4ET/M48DH051nrvlfyywHihKOP5X58J5AJwCzAWWJ8wb9GPf2z7SqAzOAgL++x8llEGfj3FfzyO9kj5uB/2/yqYyTjK/pwPF/vvPZ2p+k71mAmXAC8AiYF6q0z3Ix3Y63oNruq6tI1Kd7kHM668TrpOzgQ2pTnc/8vue+NJteY9xVa8+nzNZc63vZzlkRQzoTxn462VFbOjnuZAVMWMAyiFr4skBymHA44xaUKWYmY0DzgPu8qcNOAP4o7/KPcBF/vsL/Wn85Wf6618IPOCcCzvn1gPrgOP81zrnXK1zLgI84K+bNsysHO/E/g2Acy7inNvL/nntXga/d55FQKWZjQbOBp52zu1xzjUATwPn+MvKnXMLnfdf8vuEbaWTAFBkZgGgGNhODpwHzrkX8J6Wk2gojn1v+xhyPZWBc+4p51zUn1wEjPPf9+kYH+L1RJKTzP9VNpXxQfPrnHvOOdfmTyaet5km2Wvm9/AquzuGMnGDIJn8fha43b/G4pyrG+I0DpRk8uqAcv99BbBtCNM3oHqJsYl6i6vyrly71vcml2JAb3ItNvQml2LGgeRUPOnNYMQZVVCl3n8D/wbE/elhwN6Em9MteC1s8P9uBu+RuUCjv/6++d0+09v8dDIF2AX8zm/+eJeZlQAjnXPbAfy/I/z1+5rXsf777vPThnNuK3ALsAmvYqoReI3cOg8SDcWx720f6ehavF8eoO9lcCjXE0lOMv9X2VTGfb2OXMe7522mOWhezexoYLxz7tGhTNggSebYzgBmmNnLZrbIzM4ZstQNrGTy+m3gk2a2BXgM+OLQJC0lMu37QSrk2rW+N7kUA3qTa7GhN7kUMw5E8SQ5fY4zqqBKITM7H6hzzr2WOLuHVd1BlvV1fjoJ4DULvMM5dzTQitflqjdZVwbmjX90IV6XrTFACfDhHlbN5vMgGTmXbzO7GYgCf+ia1cNqh1oGGV8+KZZM+WVTGSedFzP7JDAP+MmgpmjwHDCvZpYH/Az4ypClaHAlc2wDeF02TgOuAO6yhLHxMkgyeb0CuNs5Nw6va8K9/jHPRtl0jRosuXat700uxYDe5Fps6E0uxYwDUTxJTp+vj7lWQOnmZOACM9uA1yzwDLwWVZV+Vy/wmsd2NQfcAowH8JdX4DWp2ze/22d6m59OtgBbnHOv+NN/xKuw2tnV/M//W5ewfl/yuoX9mxinYxl8EFjvnNvlnOsE/g84idw6DxINxbHvbR9pw7zB3s8HrvS7KELfy6Cevp9Hkpxk/q+yqYyTuo6Y2QeBm4ELnHPhIUrbQDtYXsuAOcDzfvw+AZifwYPhJnsu/8U51+l3L16Nd/ORaZLJ63V4Y/fhnFsIFALDhyR1Qy/Tvh+kQq5d63uTSzGgN7kWG3qTSzHjQBRPktPnOKMKqhRyzt3knBvnnJuEN6jis865K4HngEv81T4N/MV/P9+fxl/+rH/jOh+43H+KyGS8C8CrwGJgunlP8Srw9zF/CLKWNOfcDmCzmc30Z50JrGD/vHYvg0/5TwQ4AWj0u2g9CZxlZlV+i6SzgCf9Zc1mdoI/HsCnEraVLjYBJ5hZsZ/GrjLImfOgm6E49r3tIy34TaH/He8LXlvCoj4dY/+86Ot5JMlJ5v8qm8r4oPn1uzb8Cu+8TbtK3z44YF6dc43OueHOuUl+/F6El+clqUluvyVzLj+CNwAyZjYcr/tG7ZCmcmAkk9dNeHEYM3sf3g3FriFN5dDpLa7Ku3LtWt+bXIoBvcm12NCbXIoZB6J4kpy+xxmXBqO/6+XAawLZ9RS/KXg3neuAh4GQP7/Qn17nL5+S8Pmb8Z4ksJqEp9ThNSdc4y+7OdX57CXvRwFLgGV4F7QqvL77zwBr/b/V/roG3O7n5y0Sno6BN1bPOv91TcL8ecBy/zO3AZbqPPdQBt8BVvnpvBfvKW1Zfx4A9+ONu9WJV8N+3VAc+972kUZlsA6vv/ab/uvOQz3Gh3Ie6ZX0sXtPmQPfxftCmnVlnER+/w7sTDhv56c6zYOV127rPk+GP6kpiWNrwE/xfjx5C//JoJn4SiKvs4GX8Z7I9CZwVqrT3I+89hRfrgeuTziuPcZVvfp0zmTVtb4f5ZA1MeBQy6DbuhkfG/pxLmRNzOhnOWRNPDlAGQx4nOm6WRMREREREREREUkJdfETEREREREREZGUUgWViIiIiIiIiIiklCqoREREREREREQkpVRBJSIiIiIiIiIiKaUKKhERERGRHGBmvzWzOjNbnsS6E8zsOTN7w8yWmdm5Q5FGERHJXaqgEklgZpVm9s/93MbVZnZbEus4MzszYd5H/XmX9Gf/IiIyeBQnJMPdDZyT5LrfBB5yzh0NXA78crASJSIiAqqgEumuEnjPjYeZ5Q/Cvt4CrkiYvhxYOgj7ERGRgaM4IRnLOfcCsCdxnplNNbMnzOw1M3vRzGZ1rQ6U++8rgG1DmFQREclBqqAS2d8Pgalm9qaZLfabtt+Hd5OAmT3if4F728w+1/UhM7vGzNaY2T+AkxPm15jZn/xtLTazkxP29SJwnJkFzawUmAa8mfDZM/1m9W/5TfJD/vwNZvYdM3vdXzbLn1/ir7fY/9yF/vwXzeyohO2+bGZHDELZiYjkAsUJyTa/Br7onDsG+CrvtpT6NvBJM9sCPAZ8MTXJExGRXKEKKpH9fR14xzl3FPA14DjgZufcbH/5tf4XuHnAjWY2zMxGA9/Bu+H4EDA7YXs/B37mnDsWuBi4K2GZA/4OnA1cCMzvWmBmhXjN8D/unDscCACfT/hsvXNuLnAH3pdJgJuBZ/19nQ78xMxK/H1e7W93BhByzi07tOIREcl5ihOSNfyKz5OAh83sTeBXwGh/8RXA3c65ccC5wL1mpnsHEREZNAoyIgf2qnNufcL0jWa2FFgEjAemA8cDzzvndjnnIsCDCet/ELjN/9I3Hyg3s7KE5Q/gddm4HLg/Yf5MYL1zbo0/fQ9wSsLy//P/vgZM8t+fBXzd39fzQCEwAXgYON/MgsC1eDc0IiIyMBQnJJPlAXudc0clvN7nL7sOeAjAObcQ73wZnqJ0iohIDgikOgEiaa61642ZnYZ3I3Gic67NzJ7H+7IG3q/cPcnz129PnGlm3oece9XM5gDtzrk1XfMB48DC/t8Y7/4fG3Cxc25195XN7Gm8X98vw/tVX0REBobihGQs51yTma03s0udcw+bd4Id4ZxbCmwCzgTuNrP34Z3Lu1KZXhERyW5qQSWyv2agrJdlFUCDf9MxCzjBn/8KcJrfjSMIXJrwmaeAG7omEsf4SHAT8I1u81YBk8xsmj99FfCPg6T9SeCL/pdLzOzohGV3Ab8AFjvn9vT0YRERSYrihGQsM7sfWAjMNLMtZnYdcCVwnd/y7228ikqArwCf9effD1ztnOutolVERKTf1IJKJIFzbrc/OOxyoB3YmbD4CeB6M1sGrMbrvoFzbruZfRvvC9924HWg62lONwK3+58JAC8A13fb5+M9pKPDzK7BGxMiACwG7jxI8r8H/DewzL/52ACc72/vNTNrAn6XTDmIiEjPFCckkznnruhl0Tk9rLuChAH9RUREBpvphxCR7GdmY/DGG5nlnIunODkiIpJmFCdEREQk1dTFTyTLmdmn8LqX3KybDhER6U5xQkRERNKBWlCJiIiIiIiIiEhKqQWViIiIiIiIiIiklCqoREREREREREQkpVRBJSIiIiIiIiIiKaUKKhERERERERERSSlVUImIiIiIiIiISEr9f5r8OsCHXQUgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Labe 分布\n",
    "fig,axes = plt.subplots(2,3,figsize=(20,5))\n",
    "fig.set_size_inches(20,12)\n",
    "sns.distplot(data_train['tradeMoney'],ax=axes[0][0])\n",
    "sns.distplot(data_train[(data_train['tradeMoney']<=20000)]['tradeMoney'],ax=axes[0][1])\n",
    "sns.distplot(data_train[(data_train['tradeMoney']>20000)&(data_train['tradeMoney']<=50000)]['tradeMoney'],ax=axes[0][2])\n",
    "sns.distplot(data_train[(data_train['tradeMoney']>50000)&(data_train['tradeMoney']<=100000)]['tradeMoney'],ax=axes[1][0])\n",
    "sns.distplot(data_train[(data_train['tradeMoney']>100000)]['tradeMoney'],ax=axes[1][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-24T14:06:22.373942Z",
     "start_time": "2019-12-24T14:06:22.313139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "money<=10000 38964\n",
      "10000<money<=20000 1985\n",
      "20000<money<=50000 433\n",
      "50000<money<=100000 39\n",
      "100000<money 19\n"
     ]
    }
   ],
   "source": [
    "print(\"money<=10000\",len(data_train[(data_train['tradeMoney']<=10000)]['tradeMoney']))\n",
    "print(\"10000<money<=20000\",len(data_train[(data_train['tradeMoney']>10000)&(data_train['tradeMoney']<=20000)]['tradeMoney']))\n",
    "print(\"20000<money<=50000\",len(data_train[(data_train['tradeMoney']>20000)&(data_train['tradeMoney']<=50000)]['tradeMoney']))\n",
    "print(\"50000<money<=100000\",len(data_train[(data_train['tradeMoney']>50000)&(data_train['tradeMoney']<=100000)]['tradeMoney']))\n",
    "print(\"100000<money\",len(data_train[(data_train['tradeMoney']>100000)]['tradeMoney']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 简要分析\n",
    "将目标变量tradeMoney分组，并查看每组间的分布；  \n",
    "可以看出绝大多数都是集中在10000元以内的，并且从图中可以看到该分布是右偏的。  \n",
    "  \n",
    "这里只是一种实现方式，完全可以将tradeMoney和其他字段一起结合起来查看，比如楼层高低，地区板块。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
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